{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据\n",
    "import random\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.discriminant_analysis import StandardScaler\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn.utils import compute_class_weight\n",
    "from keras.regularizers import l1,l2\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取数据\n",
    "def read_data(file_path):\n",
    "    data = pd.read_csv(file_path,index_col=\"sample_id\")\n",
    "    if 'label' in data.columns:\n",
    "        y = data['label']\n",
    "        return data, y\n",
    "    else:\n",
    "        return data, None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入文件\n",
    "file_path_train = r'D:\\code\\Software-Cup\\python\\data\\train_10000.csv'\n",
    "file_path_val = r'D:\\code\\Software-Cup\\python\\data\\validate_1000.csv'\n",
    "df,y = read_data(file_path_train)\n",
    "df_val,y_val = read_data(file_path_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#分析数据\n",
    "def data_analysis(data,y):\n",
    "\n",
    "    # 设置绘图风格\n",
    "    sns.set(style=\"darkgrid\")   \n",
    "    # 绘制类别分布图\n",
    "    plt.rcParams['axes.unicode_minus'] = False\n",
    "    plt.rcParams['font.family'] = ['SimHei']\n",
    "    ax = sns.countplot(x=y)\n",
    "    ax.set_title('故障分布')\n",
    "    plt.xlabel('类别')\n",
    "    plt.ylabel('个数')\n",
    "    plt.show()\n",
    "    # 获取数据集中的所有特征列\n",
    "    warnings.filterwarnings('ignore')\n",
    "    data = data.drop(y.name, axis=1)\n",
    "    features = data.columns\n",
    "    plot_features = random.sample(list(features), k=1)\n",
    "    # 遍历每个特征\n",
    "    for feature in plot_features:\n",
    "        data = data.reset_index(drop=True)\n",
    "        # 绘制概率密度图\n",
    "        sns.kdeplot(data[feature], fill=True)\n",
    "        plt.title(f'{feature} 概率密度函数')\n",
    "        plt.xlabel(feature)\n",
    "        plt.ylabel('概率密度')\n",
    "        plt.show()\n",
    "    data = pd.concat([data, y], axis=1)\n",
    "\n",
    "\n",
    "    # 计算相关系数矩阵\n",
    "    corr = data.corr()\n",
    "    # 绘制热力图\n",
    "    plt.figure(figsize=(100,100))\n",
    "    sns.heatmap(corr, cmap='coolwarm', annot=True)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#特征分析\n",
    "#data_analysis(df,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除重复值\n",
    "def remove_duplicates(data, y):\n",
    "    data = data.drop_duplicates()\n",
    "    y = y.loc[data.index]\n",
    "    return data, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除重复值\n",
    "df,y = remove_duplicates(df,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除无用特征列\n",
    "def drop_features(data):\n",
    "    features_to_drop = ['feature57', 'feature77', 'feature100', 'feature92', 'feature88', 'feature65', 'feature54'\n",
    "                        , 'feature80', 'feature1', 'feature60', 'feature32', 'feature78', 'feature20'] # 在这里定义要删除的特征名称列表\n",
    "    data = data.drop(features_to_drop, axis=1)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#运行删除无用列函数\n",
    "df = drop_features(df)\n",
    "df_val = drop_features(df_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#填充缺失值\n",
    "def fill_missing_values_knn(data):\n",
    "    if 'label' in data.columns:\n",
    "        data = data.drop('label', axis=1)\n",
    "    # 使用KNNImputer填充缺失值\n",
    "    #imputer = KNNImputer(n_neighbors = 42, weights = 'distance')\n",
    "    imputer = KNNImputer(n_neighbors = 43, weights = 'distance')\n",
    "    data_filled = imputer.fit_transform(data)\n",
    "    # 将填充后的数据转换为DataFrame\n",
    "    data = pd.DataFrame(data_filled, columns=data.columns, index=data.index)\n",
    "    #data.to_csv('output.csv', index=True)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#运行填充缺失值函数\n",
    "df = fill_missing_values_knn(df)\n",
    "df_val = fill_missing_values_knn(df_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据标准化\n",
    "def scale_data(data):\n",
    "    # 进行数据标准化\n",
    "    scaler = StandardScaler()\n",
    "    data = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)\n",
    "    #data.to_csv('output.csv', index=True)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#标准化数据集\n",
    "df = scale_data(df)\n",
    "df_val  = scale_data(df_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#模型构建及训练\n",
    "def train_model(X_train, y_train, X_val = None, y_val = None):\n",
    "\n",
    "    if X_val is  None or y_val is  None:\n",
    "        X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)\n",
    "        \n",
    "    # 计算类别权重\n",
    "    class_weights = compute_class_weight(class_weight='balanced',classes= np.unique(y_train),y = y_train)\n",
    "    class_weights = dict(enumerate(class_weights))\n",
    "    \n",
    "    # 构建模型\n",
    "    model = Sequential()\n",
    "    model.add(Dense(94, activation='tanh', input_shape=(X_train.shape[1],), kernel_regularizer=l1(0.01)))\n",
    "    model.add(Dense(282, activation='tanh',kernel_regularizer=l2(0.01)))\n",
    "    model.add(Dense(252, activation='tanh',kernel_regularizer=l2(0.01)))\n",
    "    model.add(Dense(42, activation='tanh',kernel_regularizer=l2(0.01)))\n",
    "    model.add(Dense(6, activation='softmax'))\n",
    "\n",
    "    \n",
    "    # 编译模型\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='Adamax', metrics=['accuracy'])\n",
    "\n",
    "    # 将标签数据转换为独热编码\n",
    "    y_train = to_categorical(y_train)\n",
    "    y_val = to_categorical(y_val)\n",
    "\n",
    "    # 训练模型\n",
    "    history = model.fit(X_train,y_train, epochs= 312, batch_size= 1234,class_weight=class_weights, validation_data=(X_val, y_val))\n",
    "    \n",
    "    # 绘制训练损失和验证损失\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title('Model loss')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(['Train', 'Validation'], loc='upper right')\n",
    "    plt.show()\n",
    "\n",
    "    # 绘制训练准确率和验证准确率\n",
    "    plt.plot(history.history['accuracy'])\n",
    "    plt.plot(history.history['val_accuracy'])\n",
    "    plt.title('Model accuracy')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(['Train', 'Validation'], loc='lower right')\n",
    "    plt.show()\n",
    "    \n",
    "    # 在验证集上评估模型性能并输出分类报告\n",
    "    y_pred = model.predict(X_val)\n",
    "    y_pred = y_pred.argmax(axis=1)\n",
    "    y_true = y_val.argmax(axis=1)\n",
    "    print(classification_report(y_true, y_pred))\n",
    "\n",
    "    # X的混淆矩阵\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    # 绘制热力图\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "    # 设置坐标轴标签\n",
    "    plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "    plt.xlabel('预测标签')\n",
    "    plt.ylabel('真实标签')\n",
    "\n",
    "\n",
    "    # 生成唯一的模型名称\n",
    "    timestamp = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
    "    model_name = f\"dnn_model_{timestamp}.h5\"\n",
    "\n",
    "    # 保存模型\n",
    "    #model.save(model_name)\n",
    "\n",
    "    return model_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/312\n",
      "6/6 [==============================] - 2s 165ms/step - loss: 13.9796 - accuracy: 0.3108 - val_loss: 13.2442 - val_accuracy: 0.6040\n",
      "Epoch 2/312\n",
      "6/6 [==============================] - 0s 79ms/step - loss: 12.9573 - accuracy: 0.4417 - val_loss: 12.4861 - val_accuracy: 0.6620\n",
      "Epoch 3/312\n",
      "6/6 [==============================] - 0s 51ms/step - loss: 12.2438 - accuracy: 0.5127 - val_loss: 11.8316 - val_accuracy: 0.7060\n",
      "Epoch 4/312\n",
      "6/6 [==============================] - 0s 48ms/step - loss: 11.6174 - accuracy: 0.5716 - val_loss: 11.2454 - val_accuracy: 0.7380\n",
      "Epoch 5/312\n",
      "6/6 [==============================] - 0s 51ms/step - loss: 11.0538 - accuracy: 0.6104 - val_loss: 10.7152 - val_accuracy: 0.7480\n",
      "Epoch 6/312\n",
      "6/6 [==============================] - 0s 49ms/step - loss: 10.5371 - accuracy: 0.6439 - val_loss: 10.2287 - val_accuracy: 0.7610\n",
      "Epoch 7/312\n",
      "6/6 [==============================] - 0s 54ms/step - loss: 10.0591 - accuracy: 0.6763 - val_loss: 9.7744 - val_accuracy: 0.7780\n",
      "Epoch 8/312\n",
      "6/6 [==============================] - 0s 66ms/step - loss: 9.6126 - accuracy: 0.6963 - val_loss: 9.3492 - val_accuracy: 0.7820\n",
      "Epoch 9/312\n",
      "6/6 [==============================] - 0s 56ms/step - loss: 9.1946 - accuracy: 0.7090 - val_loss: 8.9474 - val_accuracy: 0.7930\n",
      "Epoch 10/312\n",
      "6/6 [==============================] - 0s 57ms/step - loss: 8.7988 - accuracy: 0.7160 - val_loss: 8.5641 - val_accuracy: 0.8010\n",
      "Epoch 11/312\n",
      "6/6 [==============================] - 0s 65ms/step - loss: 8.4242 - accuracy: 0.7265 - val_loss: 8.2046 - val_accuracy: 0.8050\n",
      "Epoch 12/312\n",
      "6/6 [==============================] - 1s 108ms/step - loss: 8.0679 - accuracy: 0.7362 - val_loss: 7.8632 - val_accuracy: 0.8090\n",
      "Epoch 13/312\n",
      "6/6 [==============================] - 0s 61ms/step - loss: 7.7292 - accuracy: 0.7382 - val_loss: 7.5384 - val_accuracy: 0.8120\n",
      "Epoch 14/312\n",
      "6/6 [==============================] - 0s 64ms/step - loss: 7.4096 - accuracy: 0.7413 - val_loss: 7.2303 - val_accuracy: 0.8220\n",
      "Epoch 15/312\n",
      "6/6 [==============================] - 0s 62ms/step - loss: 7.1076 - accuracy: 0.7449 - val_loss: 6.9387 - val_accuracy: 0.8260\n",
      "Epoch 16/312\n",
      "6/6 [==============================] - 0s 63ms/step - loss: 6.8198 - accuracy: 0.7525 - val_loss: 6.6593 - val_accuracy: 0.8290\n",
      "Epoch 17/312\n",
      "6/6 [==============================] - 0s 59ms/step - loss: 6.5466 - accuracy: 0.7600 - val_loss: 6.3931 - val_accuracy: 0.8290\n",
      "Epoch 18/312\n",
      "6/6 [==============================] - 0s 62ms/step - loss: 6.2879 - accuracy: 0.7622 - val_loss: 6.1424 - val_accuracy: 0.8330\n",
      "Epoch 19/312\n",
      "6/6 [==============================] - 0s 64ms/step - loss: 6.0416 - accuracy: 0.7619 - val_loss: 5.9029 - val_accuracy: 0.8380\n",
      "Epoch 20/312\n",
      "6/6 [==============================] - 0s 59ms/step - loss: 5.8060 - accuracy: 0.7656 - val_loss: 5.6726 - val_accuracy: 0.8370\n",
      "Epoch 21/312\n",
      "6/6 [==============================] - 0s 63ms/step - loss: 5.5797 - accuracy: 0.7711 - val_loss: 5.4514 - val_accuracy: 0.8350\n",
      "Epoch 22/312\n",
      "6/6 [==============================] - 1s 62ms/step - loss: 5.3639 - accuracy: 0.7741 - val_loss: 5.2429 - val_accuracy: 0.8360\n",
      "Epoch 23/312\n",
      "6/6 [==============================] - 0s 66ms/step - loss: 5.1588 - accuracy: 0.7754 - val_loss: 5.0447 - val_accuracy: 0.8380\n",
      "Epoch 24/312\n",
      "6/6 [==============================] - 0s 64ms/step - loss: 4.9635 - accuracy: 0.7718 - val_loss: 4.8538 - val_accuracy: 0.8400\n",
      "Epoch 25/312\n",
      "6/6 [==============================] - 0s 58ms/step - loss: 4.7760 - accuracy: 0.7726 - val_loss: 4.6699 - val_accuracy: 0.8410\n",
      "Epoch 26/312\n",
      "6/6 [==============================] - 0s 62ms/step - loss: 4.5964 - accuracy: 0.7730 - val_loss: 4.4966 - val_accuracy: 0.8430\n",
      "Epoch 27/312\n",
      "6/6 [==============================] - 0s 58ms/step - loss: 4.4248 - accuracy: 0.7745 - val_loss: 4.3308 - val_accuracy: 0.8450\n",
      "Epoch 28/312\n",
      "6/6 [==============================] - 0s 61ms/step - loss: 4.2618 - accuracy: 0.7741 - val_loss: 4.1722 - val_accuracy: 0.8490\n",
      "Epoch 29/312\n",
      "6/6 [==============================] - 0s 61ms/step - loss: 4.1071 - accuracy: 0.7743 - val_loss: 4.0210 - val_accuracy: 0.8480\n",
      "Epoch 30/312\n",
      "6/6 [==============================] - 0s 61ms/step - loss: 3.9584 - accuracy: 0.7748 - val_loss: 3.8779 - val_accuracy: 0.8500\n",
      "Epoch 31/312\n",
      "6/6 [==============================] - 1s 90ms/step - loss: 3.8154 - accuracy: 0.7778 - val_loss: 3.7436 - val_accuracy: 0.8470\n",
      "Epoch 32/312\n",
      "6/6 [==============================] - 0s 61ms/step - loss: 3.6787 - accuracy: 0.7802 - val_loss: 3.6133 - val_accuracy: 0.8480\n",
      "Epoch 33/312\n",
      "6/6 [==============================] - 0s 60ms/step - loss: 3.5485 - accuracy: 0.7756 - val_loss: 3.4857 - val_accuracy: 0.8520\n",
      "Epoch 34/312\n",
      "6/6 [==============================] - 0s 63ms/step - loss: 3.4251 - accuracy: 0.7740 - val_loss: 3.3626 - val_accuracy: 0.8530\n",
      "Epoch 35/312\n",
      "6/6 [==============================] - 0s 59ms/step - loss: 3.3070 - accuracy: 0.7762 - val_loss: 3.2449 - val_accuracy: 0.8500\n",
      "Epoch 36/312\n",
      "6/6 [==============================] - 0s 66ms/step - loss: 3.1945 - accuracy: 0.7792 - val_loss: 3.1376 - val_accuracy: 0.8510\n",
      "Epoch 37/312\n",
      "6/6 [==============================] - 0s 58ms/step - loss: 3.0873 - accuracy: 0.7775 - val_loss: 3.0353 - val_accuracy: 0.8570\n",
      "Epoch 38/312\n",
      "6/6 [==============================] - 0s 57ms/step - loss: 2.9852 - accuracy: 0.7760 - val_loss: 2.9381 - val_accuracy: 0.8550\n",
      "Epoch 39/312\n",
      "6/6 [==============================] - 1s 93ms/step - loss: 2.8889 - accuracy: 0.7722 - val_loss: 2.8444 - val_accuracy: 0.8570\n",
      "Epoch 40/312\n",
      "6/6 [==============================] - 0s 61ms/step - loss: 2.7958 - accuracy: 0.7753 - val_loss: 2.7551 - val_accuracy: 0.8590\n",
      "Epoch 41/312\n",
      "6/6 [==============================] - 1s 82ms/step - loss: 2.7074 - accuracy: 0.7789 - val_loss: 2.6711 - val_accuracy: 0.8550\n",
      "Epoch 42/312\n",
      "6/6 [==============================] - 0s 58ms/step - loss: 2.6240 - accuracy: 0.7813 - val_loss: 2.5904 - val_accuracy: 0.8540\n",
      "Epoch 43/312\n",
      "6/6 [==============================] - 0s 57ms/step - loss: 2.5443 - accuracy: 0.7800 - val_loss: 2.5140 - val_accuracy: 0.8580\n",
      "Epoch 44/312\n",
      "6/6 [==============================] - 0s 59ms/step - loss: 2.4669 - accuracy: 0.7799 - val_loss: 2.4410 - val_accuracy: 0.8600\n",
      "Epoch 45/312\n",
      "6/6 [==============================] - 0s 68ms/step - loss: 2.3941 - accuracy: 0.7794 - val_loss: 2.3731 - val_accuracy: 0.8550\n",
      "Epoch 46/312\n",
      "6/6 [==============================] - 0s 66ms/step - loss: 2.3245 - accuracy: 0.7794 - val_loss: 2.3080 - val_accuracy: 0.8560\n",
      "Epoch 47/312\n",
      "6/6 [==============================] - 0s 58ms/step - loss: 2.2585 - accuracy: 0.7768 - val_loss: 2.2422 - val_accuracy: 0.8570\n",
      "Epoch 48/312\n",
      "6/6 [==============================] - 0s 62ms/step - loss: 2.1953 - accuracy: 0.7741 - val_loss: 2.1772 - val_accuracy: 0.8590\n",
      "Epoch 49/312\n",
      "6/6 [==============================] - 0s 61ms/step - loss: 2.1353 - accuracy: 0.7773 - val_loss: 2.1188 - val_accuracy: 0.8550\n",
      "Epoch 50/312\n",
      "6/6 [==============================] - 1s 91ms/step - loss: 2.0776 - accuracy: 0.7795 - val_loss: 2.0653 - val_accuracy: 0.8580\n",
      "Epoch 51/312\n",
      "6/6 [==============================] - 0s 63ms/step - loss: 2.0232 - accuracy: 0.7780 - val_loss: 2.0153 - val_accuracy: 0.8580\n",
      "Epoch 52/312\n",
      "6/6 [==============================] - 0s 56ms/step - loss: 1.9719 - accuracy: 0.7772 - val_loss: 1.9644 - val_accuracy: 0.8570\n",
      "Epoch 53/312\n",
      "6/6 [==============================] - 0s 55ms/step - loss: 1.9226 - accuracy: 0.7775 - val_loss: 1.9147 - val_accuracy: 0.8570\n",
      "Epoch 54/312\n",
      "6/6 [==============================] - 0s 65ms/step - loss: 1.8752 - accuracy: 0.7786 - val_loss: 1.8699 - val_accuracy: 0.8570\n",
      "Epoch 55/312\n",
      "6/6 [==============================] - 0s 59ms/step - loss: 1.8314 - accuracy: 0.7794 - val_loss: 1.8268 - val_accuracy: 0.8570\n",
      "Epoch 56/312\n",
      "6/6 [==============================] - 0s 58ms/step - loss: 1.7891 - accuracy: 0.7749 - val_loss: 1.7908 - val_accuracy: 0.8530\n",
      "Epoch 57/312\n",
      "6/6 [==============================] - 0s 63ms/step - loss: 1.7490 - accuracy: 0.7751 - val_loss: 1.7586 - val_accuracy: 0.8480\n",
      "Epoch 58/312\n",
      "6/6 [==============================] - 0s 75ms/step - loss: 1.7117 - accuracy: 0.7808 - val_loss: 1.7239 - val_accuracy: 0.8460\n",
      "Epoch 59/312\n",
      "6/6 [==============================] - 1s 85ms/step - loss: 1.6757 - accuracy: 0.7856 - val_loss: 1.6859 - val_accuracy: 0.8480\n",
      "Epoch 60/312\n",
      "6/6 [==============================] - 0s 64ms/step - loss: 1.6411 - accuracy: 0.7791 - val_loss: 1.6507 - val_accuracy: 0.8530\n",
      "Epoch 61/312\n",
      "6/6 [==============================] - 0s 59ms/step - loss: 1.6077 - accuracy: 0.7729 - val_loss: 1.6204 - val_accuracy: 0.8540\n",
      "Epoch 62/312\n",
      "6/6 [==============================] - 0s 68ms/step - loss: 1.5765 - accuracy: 0.7732 - val_loss: 1.5897 - val_accuracy: 0.8530\n",
      "Epoch 63/312\n",
      "6/6 [==============================] - 0s 70ms/step - loss: 1.5478 - accuracy: 0.7737 - val_loss: 1.5558 - val_accuracy: 0.8520\n",
      "Epoch 64/312\n",
      "6/6 [==============================] - 0s 72ms/step - loss: 1.5198 - accuracy: 0.7773 - val_loss: 1.5289 - val_accuracy: 0.8480\n",
      "Epoch 65/312\n",
      "6/6 [==============================] - 1s 62ms/step - loss: 1.4929 - accuracy: 0.7789 - val_loss: 1.5063 - val_accuracy: 0.8490\n",
      "Epoch 66/312\n",
      "6/6 [==============================] - 1s 92ms/step - loss: 1.4674 - accuracy: 0.7786 - val_loss: 1.4835 - val_accuracy: 0.8460\n",
      "Epoch 67/312\n",
      "6/6 [==============================] - 1s 69ms/step - loss: 1.4431 - accuracy: 0.7781 - val_loss: 1.4619 - val_accuracy: 0.8450\n",
      "Epoch 68/312\n",
      "6/6 [==============================] - 0s 69ms/step - loss: 1.4227 - accuracy: 0.7741 - val_loss: 1.4427 - val_accuracy: 0.8470\n",
      "Epoch 69/312\n",
      "6/6 [==============================] - 0s 77ms/step - loss: 1.4030 - accuracy: 0.7748 - val_loss: 1.4193 - val_accuracy: 0.8490\n",
      "Epoch 70/312\n",
      "6/6 [==============================] - 0s 71ms/step - loss: 1.3830 - accuracy: 0.7797 - val_loss: 1.3997 - val_accuracy: 0.8460\n",
      "Epoch 71/312\n",
      "6/6 [==============================] - 0s 76ms/step - loss: 1.3641 - accuracy: 0.7799 - val_loss: 1.3791 - val_accuracy: 0.8490\n",
      "Epoch 72/312\n",
      "6/6 [==============================] - 0s 62ms/step - loss: 1.3459 - accuracy: 0.7767 - val_loss: 1.3627 - val_accuracy: 0.8490\n",
      "Epoch 73/312\n",
      "6/6 [==============================] - 0s 59ms/step - loss: 1.3284 - accuracy: 0.7760 - val_loss: 1.3478 - val_accuracy: 0.8490\n",
      "Epoch 74/312\n",
      "6/6 [==============================] - 0s 67ms/step - loss: 1.3115 - accuracy: 0.7768 - val_loss: 1.3333 - val_accuracy: 0.8500\n",
      "Epoch 75/312\n",
      "6/6 [==============================] - 1s 100ms/step - loss: 1.2960 - accuracy: 0.7759 - val_loss: 1.3208 - val_accuracy: 0.8490\n",
      "Epoch 76/312\n",
      "6/6 [==============================] - 0s 76ms/step - loss: 1.2822 - accuracy: 0.7741 - val_loss: 1.3088 - val_accuracy: 0.8470\n",
      "Epoch 77/312\n",
      "6/6 [==============================] - 1s 118ms/step - loss: 1.2675 - accuracy: 0.7757 - val_loss: 1.2968 - val_accuracy: 0.8510\n",
      "Epoch 78/312\n",
      "6/6 [==============================] - 0s 57ms/step - loss: 1.2535 - accuracy: 0.7792 - val_loss: 1.2853 - val_accuracy: 0.8460\n",
      "Epoch 79/312\n",
      "6/6 [==============================] - 0s 51ms/step - loss: 1.2392 - accuracy: 0.7811 - val_loss: 1.2692 - val_accuracy: 0.8470\n",
      "Epoch 80/312\n",
      "6/6 [==============================] - 0s 54ms/step - loss: 1.2270 - accuracy: 0.7759 - val_loss: 1.2521 - val_accuracy: 0.8470\n",
      "Epoch 81/312\n",
      "6/6 [==============================] - 0s 52ms/step - loss: 1.2148 - accuracy: 0.7733 - val_loss: 1.2443 - val_accuracy: 0.8480\n",
      "Epoch 82/312\n",
      "6/6 [==============================] - 0s 56ms/step - loss: 1.2026 - accuracy: 0.7740 - val_loss: 1.2309 - val_accuracy: 0.8480\n",
      "Epoch 83/312\n",
      "6/6 [==============================] - 1s 98ms/step - loss: 1.1905 - accuracy: 0.7780 - val_loss: 1.2206 - val_accuracy: 0.8430\n",
      "Epoch 84/312\n",
      "6/6 [==============================] - 0s 55ms/step - loss: 1.1801 - accuracy: 0.7760 - val_loss: 1.2102 - val_accuracy: 0.8430\n",
      "Epoch 85/312\n",
      "6/6 [==============================] - 0s 55ms/step - loss: 1.1698 - accuracy: 0.7772 - val_loss: 1.1983 - val_accuracy: 0.8470\n",
      "Epoch 86/312\n",
      "6/6 [==============================] - 0s 54ms/step - loss: 1.1595 - accuracy: 0.7768 - val_loss: 1.1884 - val_accuracy: 0.8490\n",
      "Epoch 87/312\n",
      "6/6 [==============================] - 0s 61ms/step - loss: 1.1501 - accuracy: 0.7746 - val_loss: 1.1795 - val_accuracy: 0.8480\n",
      "Epoch 88/312\n",
      "6/6 [==============================] - 0s 79ms/step - loss: 1.1413 - accuracy: 0.7778 - val_loss: 1.1745 - val_accuracy: 0.8440\n",
      "Epoch 89/312\n",
      "6/6 [==============================] - 1s 111ms/step - loss: 1.1332 - accuracy: 0.7837 - val_loss: 1.1685 - val_accuracy: 0.8420\n",
      "Epoch 90/312\n",
      "6/6 [==============================] - 1s 92ms/step - loss: 1.1241 - accuracy: 0.7819 - val_loss: 1.1587 - val_accuracy: 0.8450\n",
      "Epoch 91/312\n",
      "6/6 [==============================] - 1s 185ms/step - loss: 1.1155 - accuracy: 0.7776 - val_loss: 1.1484 - val_accuracy: 0.8450\n",
      "Epoch 92/312\n",
      "6/6 [==============================] - 0s 78ms/step - loss: 1.1081 - accuracy: 0.7724 - val_loss: 1.1401 - val_accuracy: 0.8460\n",
      "Epoch 93/312\n",
      "6/6 [==============================] - 0s 67ms/step - loss: 1.1004 - accuracy: 0.7727 - val_loss: 1.1326 - val_accuracy: 0.8480\n",
      "Epoch 94/312\n",
      "6/6 [==============================] - 0s 82ms/step - loss: 1.0942 - accuracy: 0.7737 - val_loss: 1.1283 - val_accuracy: 0.8450\n",
      "Epoch 95/312\n",
      "6/6 [==============================] - 0s 62ms/step - loss: 1.0881 - accuracy: 0.7738 - val_loss: 1.1225 - val_accuracy: 0.8490\n",
      "Epoch 96/312\n",
      "6/6 [==============================] - 0s 62ms/step - loss: 1.0809 - accuracy: 0.7772 - val_loss: 1.1128 - val_accuracy: 0.8510\n",
      "Epoch 97/312\n",
      "6/6 [==============================] - 0s 59ms/step - loss: 1.0742 - accuracy: 0.7787 - val_loss: 1.1036 - val_accuracy: 0.8530\n",
      "Epoch 98/312\n",
      "6/6 [==============================] - 0s 67ms/step - loss: 1.0679 - accuracy: 0.7760 - val_loss: 1.0980 - val_accuracy: 0.8520\n",
      "Epoch 99/312\n",
      "6/6 [==============================] - 1s 68ms/step - loss: 1.0617 - accuracy: 0.7775 - val_loss: 1.0931 - val_accuracy: 0.8530\n",
      "Epoch 100/312\n",
      "6/6 [==============================] - 0s 67ms/step - loss: 1.0558 - accuracy: 0.7776 - val_loss: 1.0919 - val_accuracy: 0.8480\n",
      "Epoch 101/312\n",
      "6/6 [==============================] - 0s 64ms/step - loss: 1.0498 - accuracy: 0.7765 - val_loss: 1.0815 - val_accuracy: 0.8530\n",
      "Epoch 102/312\n",
      "6/6 [==============================] - 0s 59ms/step - loss: 1.0439 - accuracy: 0.7760 - val_loss: 1.0771 - val_accuracy: 0.8490\n",
      "Epoch 103/312\n",
      "6/6 [==============================] - 0s 57ms/step - loss: 1.0388 - accuracy: 0.7768 - val_loss: 1.0753 - val_accuracy: 0.8460\n",
      "Epoch 104/312\n",
      "6/6 [==============================] - 1s 116ms/step - loss: 1.0336 - accuracy: 0.7799 - val_loss: 1.0728 - val_accuracy: 0.8430\n",
      "Epoch 105/312\n",
      "6/6 [==============================] - 1s 99ms/step - loss: 1.0287 - accuracy: 0.7816 - val_loss: 1.0669 - val_accuracy: 0.8460\n",
      "Epoch 106/312\n",
      "6/6 [==============================] - 0s 62ms/step - loss: 1.0229 - accuracy: 0.7803 - val_loss: 1.0578 - val_accuracy: 0.8470\n",
      "Epoch 107/312\n",
      "6/6 [==============================] - 0s 66ms/step - loss: 1.0193 - accuracy: 0.7760 - val_loss: 1.0494 - val_accuracy: 0.8530\n",
      "Epoch 108/312\n",
      "6/6 [==============================] - 0s 78ms/step - loss: 1.0144 - accuracy: 0.7748 - val_loss: 1.0450 - val_accuracy: 0.8540\n",
      "Epoch 109/312\n",
      "6/6 [==============================] - 1s 107ms/step - loss: 1.0103 - accuracy: 0.7772 - val_loss: 1.0435 - val_accuracy: 0.8530\n",
      "Epoch 110/312\n",
      "6/6 [==============================] - 0s 73ms/step - loss: 1.0063 - accuracy: 0.7807 - val_loss: 1.0414 - val_accuracy: 0.8520\n",
      "Epoch 111/312\n",
      "6/6 [==============================] - 1s 100ms/step - loss: 1.0017 - accuracy: 0.7784 - val_loss: 1.0389 - val_accuracy: 0.8480\n",
      "Epoch 112/312\n",
      "6/6 [==============================] - 0s 64ms/step - loss: 0.9979 - accuracy: 0.7764 - val_loss: 1.0344 - val_accuracy: 0.8490\n",
      "Epoch 113/312\n",
      "6/6 [==============================] - 0s 65ms/step - loss: 0.9943 - accuracy: 0.7819 - val_loss: 1.0353 - val_accuracy: 0.8490\n",
      "Epoch 114/312\n",
      "6/6 [==============================] - 0s 54ms/step - loss: 0.9907 - accuracy: 0.7834 - val_loss: 1.0295 - val_accuracy: 0.8540\n",
      "Epoch 115/312\n",
      "6/6 [==============================] - 0s 52ms/step - loss: 0.9868 - accuracy: 0.7795 - val_loss: 1.0245 - val_accuracy: 0.8560\n",
      "Epoch 116/312\n",
      "6/6 [==============================] - 0s 85ms/step - loss: 0.9834 - accuracy: 0.7741 - val_loss: 1.0225 - val_accuracy: 0.8530\n",
      "Epoch 117/312\n",
      "6/6 [==============================] - 0s 55ms/step - loss: 0.9800 - accuracy: 0.7753 - val_loss: 1.0204 - val_accuracy: 0.8480\n",
      "Epoch 118/312\n",
      "6/6 [==============================] - 0s 54ms/step - loss: 0.9764 - accuracy: 0.7797 - val_loss: 1.0152 - val_accuracy: 0.8510\n",
      "Epoch 119/312\n",
      "6/6 [==============================] - 1s 98ms/step - loss: 0.9735 - accuracy: 0.7849 - val_loss: 1.0113 - val_accuracy: 0.8480\n",
      "Epoch 120/312\n",
      "6/6 [==============================] - 0s 59ms/step - loss: 0.9711 - accuracy: 0.7789 - val_loss: 1.0085 - val_accuracy: 0.8490\n",
      "Epoch 121/312\n",
      "6/6 [==============================] - 0s 86ms/step - loss: 0.9679 - accuracy: 0.7740 - val_loss: 1.0029 - val_accuracy: 0.8510\n",
      "Epoch 122/312\n",
      "6/6 [==============================] - 1s 88ms/step - loss: 0.9653 - accuracy: 0.7721 - val_loss: 0.9976 - val_accuracy: 0.8560\n",
      "Epoch 123/312\n",
      "6/6 [==============================] - 0s 62ms/step - loss: 0.9618 - accuracy: 0.7759 - val_loss: 0.9954 - val_accuracy: 0.8560\n",
      "Epoch 124/312\n",
      "6/6 [==============================] - 1s 79ms/step - loss: 0.9590 - accuracy: 0.7775 - val_loss: 0.9930 - val_accuracy: 0.8530\n",
      "Epoch 125/312\n",
      "6/6 [==============================] - 1s 94ms/step - loss: 0.9559 - accuracy: 0.7794 - val_loss: 0.9911 - val_accuracy: 0.8560\n",
      "Epoch 126/312\n",
      "6/6 [==============================] - 0s 51ms/step - loss: 0.9524 - accuracy: 0.7822 - val_loss: 0.9905 - val_accuracy: 0.8550\n",
      "Epoch 127/312\n",
      "6/6 [==============================] - 0s 53ms/step - loss: 0.9500 - accuracy: 0.7799 - val_loss: 0.9880 - val_accuracy: 0.8550\n",
      "Epoch 128/312\n",
      "6/6 [==============================] - 0s 71ms/step - loss: 0.9478 - accuracy: 0.7805 - val_loss: 0.9853 - val_accuracy: 0.8560\n",
      "Epoch 129/312\n",
      "6/6 [==============================] - 0s 54ms/step - loss: 0.9458 - accuracy: 0.7832 - val_loss: 0.9823 - val_accuracy: 0.8520\n",
      "Epoch 130/312\n",
      "6/6 [==============================] - 0s 56ms/step - loss: 0.9440 - accuracy: 0.7837 - val_loss: 0.9768 - val_accuracy: 0.8510\n",
      "Epoch 131/312\n",
      "6/6 [==============================] - 1s 140ms/step - loss: 0.9410 - accuracy: 0.7816 - val_loss: 0.9747 - val_accuracy: 0.8530\n",
      "Epoch 132/312\n",
      "6/6 [==============================] - 0s 56ms/step - loss: 0.9378 - accuracy: 0.7784 - val_loss: 0.9778 - val_accuracy: 0.8570\n",
      "Epoch 133/312\n",
      "6/6 [==============================] - 0s 58ms/step - loss: 0.9358 - accuracy: 0.7776 - val_loss: 0.9769 - val_accuracy: 0.8520\n",
      "Epoch 134/312\n",
      "6/6 [==============================] - 1s 85ms/step - loss: 0.9335 - accuracy: 0.7748 - val_loss: 0.9714 - val_accuracy: 0.8560\n",
      "Epoch 135/312\n",
      "6/6 [==============================] - 0s 74ms/step - loss: 0.9312 - accuracy: 0.7729 - val_loss: 0.9694 - val_accuracy: 0.8560\n",
      "Epoch 136/312\n",
      "6/6 [==============================] - 0s 68ms/step - loss: 0.9284 - accuracy: 0.7780 - val_loss: 0.9631 - val_accuracy: 0.8570\n",
      "Epoch 137/312\n",
      "6/6 [==============================] - 0s 74ms/step - loss: 0.9267 - accuracy: 0.7822 - val_loss: 0.9608 - val_accuracy: 0.8540\n",
      "Epoch 138/312\n",
      "6/6 [==============================] - 1s 143ms/step - loss: 0.9244 - accuracy: 0.7810 - val_loss: 0.9633 - val_accuracy: 0.8560\n",
      "Epoch 139/312\n",
      "6/6 [==============================] - 1s 95ms/step - loss: 0.9222 - accuracy: 0.7807 - val_loss: 0.9637 - val_accuracy: 0.8560\n",
      "Epoch 140/312\n",
      "6/6 [==============================] - 1s 91ms/step - loss: 0.9207 - accuracy: 0.7811 - val_loss: 0.9604 - val_accuracy: 0.8530\n",
      "Epoch 141/312\n",
      "6/6 [==============================] - 1s 90ms/step - loss: 0.9188 - accuracy: 0.7821 - val_loss: 0.9567 - val_accuracy: 0.8550\n",
      "Epoch 142/312\n",
      "6/6 [==============================] - 1s 88ms/step - loss: 0.9171 - accuracy: 0.7754 - val_loss: 0.9549 - val_accuracy: 0.8530\n",
      "Epoch 143/312\n",
      "6/6 [==============================] - 1s 84ms/step - loss: 0.9159 - accuracy: 0.7732 - val_loss: 0.9536 - val_accuracy: 0.8540\n",
      "Epoch 144/312\n",
      "6/6 [==============================] - 1s 82ms/step - loss: 0.9128 - accuracy: 0.7780 - val_loss: 0.9509 - val_accuracy: 0.8580\n",
      "Epoch 145/312\n",
      "6/6 [==============================] - 1s 141ms/step - loss: 0.9117 - accuracy: 0.7794 - val_loss: 0.9500 - val_accuracy: 0.8550\n",
      "Epoch 146/312\n",
      "6/6 [==============================] - 1s 85ms/step - loss: 0.9105 - accuracy: 0.7824 - val_loss: 0.9457 - val_accuracy: 0.8550\n",
      "Epoch 147/312\n",
      "6/6 [==============================] - 1s 90ms/step - loss: 0.9073 - accuracy: 0.7797 - val_loss: 0.9442 - val_accuracy: 0.8550\n",
      "Epoch 148/312\n",
      "6/6 [==============================] - 1s 87ms/step - loss: 0.9053 - accuracy: 0.7813 - val_loss: 0.9438 - val_accuracy: 0.8590\n",
      "Epoch 149/312\n",
      "6/6 [==============================] - 1s 102ms/step - loss: 0.9034 - accuracy: 0.7838 - val_loss: 0.9422 - val_accuracy: 0.8610\n",
      "Epoch 150/312\n",
      "6/6 [==============================] - 1s 88ms/step - loss: 0.9018 - accuracy: 0.7837 - val_loss: 0.9418 - val_accuracy: 0.8560\n",
      "Epoch 151/312\n",
      "6/6 [==============================] - 1s 125ms/step - loss: 0.9005 - accuracy: 0.7802 - val_loss: 0.9381 - val_accuracy: 0.8580\n",
      "Epoch 152/312\n",
      "6/6 [==============================] - 1s 89ms/step - loss: 0.8986 - accuracy: 0.7787 - val_loss: 0.9394 - val_accuracy: 0.8580\n",
      "Epoch 153/312\n",
      "6/6 [==============================] - 1s 95ms/step - loss: 0.8972 - accuracy: 0.7778 - val_loss: 0.9375 - val_accuracy: 0.8530\n",
      "Epoch 154/312\n",
      "6/6 [==============================] - 1s 84ms/step - loss: 0.8957 - accuracy: 0.7849 - val_loss: 0.9323 - val_accuracy: 0.8590\n",
      "Epoch 155/312\n",
      "6/6 [==============================] - 1s 95ms/step - loss: 0.8943 - accuracy: 0.7832 - val_loss: 0.9310 - val_accuracy: 0.8530\n",
      "Epoch 156/312\n",
      "6/6 [==============================] - 1s 87ms/step - loss: 0.8931 - accuracy: 0.7794 - val_loss: 0.9313 - val_accuracy: 0.8610\n",
      "Epoch 157/312\n",
      "6/6 [==============================] - 1s 128ms/step - loss: 0.8914 - accuracy: 0.7803 - val_loss: 0.9307 - val_accuracy: 0.8590\n",
      "Epoch 158/312\n",
      "6/6 [==============================] - 1s 82ms/step - loss: 0.8896 - accuracy: 0.7873 - val_loss: 0.9326 - val_accuracy: 0.8550\n",
      "Epoch 159/312\n",
      "6/6 [==============================] - 1s 93ms/step - loss: 0.8886 - accuracy: 0.7846 - val_loss: 0.9336 - val_accuracy: 0.8530\n",
      "Epoch 160/312\n",
      "6/6 [==============================] - 1s 85ms/step - loss: 0.8877 - accuracy: 0.7807 - val_loss: 0.9297 - val_accuracy: 0.8560\n",
      "Epoch 161/312\n",
      "6/6 [==============================] - 1s 137ms/step - loss: 0.8858 - accuracy: 0.7822 - val_loss: 0.9271 - val_accuracy: 0.8560\n",
      "Epoch 162/312\n",
      "6/6 [==============================] - 1s 87ms/step - loss: 0.8840 - accuracy: 0.7841 - val_loss: 0.9236 - val_accuracy: 0.8500\n",
      "Epoch 163/312\n",
      "6/6 [==============================] - 1s 85ms/step - loss: 0.8826 - accuracy: 0.7791 - val_loss: 0.9226 - val_accuracy: 0.8540\n",
      "Epoch 164/312\n",
      "6/6 [==============================] - 1s 89ms/step - loss: 0.8813 - accuracy: 0.7800 - val_loss: 0.9218 - val_accuracy: 0.8590\n",
      "Epoch 165/312\n",
      "6/6 [==============================] - 1s 84ms/step - loss: 0.8805 - accuracy: 0.7830 - val_loss: 0.9221 - val_accuracy: 0.8550\n",
      "Epoch 166/312\n",
      "6/6 [==============================] - 1s 100ms/step - loss: 0.8795 - accuracy: 0.7840 - val_loss: 0.9192 - val_accuracy: 0.8520\n",
      "Epoch 167/312\n",
      "6/6 [==============================] - 1s 99ms/step - loss: 0.8777 - accuracy: 0.7822 - val_loss: 0.9157 - val_accuracy: 0.8550\n",
      "Epoch 168/312\n",
      "6/6 [==============================] - 1s 85ms/step - loss: 0.8760 - accuracy: 0.7843 - val_loss: 0.9152 - val_accuracy: 0.8580\n",
      "Epoch 169/312\n",
      "6/6 [==============================] - 1s 91ms/step - loss: 0.8746 - accuracy: 0.7856 - val_loss: 0.9112 - val_accuracy: 0.8570\n",
      "Epoch 170/312\n",
      "6/6 [==============================] - 1s 93ms/step - loss: 0.8733 - accuracy: 0.7843 - val_loss: 0.9109 - val_accuracy: 0.8590\n",
      "Epoch 171/312\n",
      "6/6 [==============================] - 1s 91ms/step - loss: 0.8728 - accuracy: 0.7824 - val_loss: 0.9086 - val_accuracy: 0.8600\n",
      "Epoch 172/312\n",
      "6/6 [==============================] - 1s 87ms/step - loss: 0.8709 - accuracy: 0.7797 - val_loss: 0.9105 - val_accuracy: 0.8610\n",
      "Epoch 173/312\n",
      "6/6 [==============================] - 1s 91ms/step - loss: 0.8699 - accuracy: 0.7832 - val_loss: 0.9102 - val_accuracy: 0.8600\n",
      "Epoch 174/312\n",
      "6/6 [==============================] - 1s 85ms/step - loss: 0.8694 - accuracy: 0.7826 - val_loss: 0.9067 - val_accuracy: 0.8580\n",
      "Epoch 175/312\n",
      "6/6 [==============================] - 1s 86ms/step - loss: 0.8684 - accuracy: 0.7794 - val_loss: 0.9079 - val_accuracy: 0.8590\n",
      "Epoch 176/312\n",
      "6/6 [==============================] - 1s 87ms/step - loss: 0.8660 - accuracy: 0.7840 - val_loss: 0.9093 - val_accuracy: 0.8540\n",
      "Epoch 177/312\n",
      "6/6 [==============================] - 1s 93ms/step - loss: 0.8654 - accuracy: 0.7872 - val_loss: 0.9039 - val_accuracy: 0.8580\n",
      "Epoch 178/312\n",
      "6/6 [==============================] - 1s 86ms/step - loss: 0.8642 - accuracy: 0.7853 - val_loss: 0.8983 - val_accuracy: 0.8590\n",
      "Epoch 179/312\n",
      "6/6 [==============================] - 1s 88ms/step - loss: 0.8634 - accuracy: 0.7800 - val_loss: 0.8982 - val_accuracy: 0.8570\n",
      "Epoch 180/312\n",
      "6/6 [==============================] - 1s 95ms/step - loss: 0.8623 - accuracy: 0.7867 - val_loss: 0.9013 - val_accuracy: 0.8550\n",
      "Epoch 181/312\n",
      "6/6 [==============================] - 1s 95ms/step - loss: 0.8602 - accuracy: 0.7897 - val_loss: 0.8966 - val_accuracy: 0.8570\n",
      "Epoch 182/312\n",
      "6/6 [==============================] - 1s 91ms/step - loss: 0.8593 - accuracy: 0.7886 - val_loss: 0.8957 - val_accuracy: 0.8580\n",
      "Epoch 183/312\n",
      "6/6 [==============================] - 1s 105ms/step - loss: 0.8576 - accuracy: 0.7851 - val_loss: 0.8983 - val_accuracy: 0.8520\n",
      "Epoch 184/312\n",
      "6/6 [==============================] - 1s 88ms/step - loss: 0.8579 - accuracy: 0.7834 - val_loss: 0.8960 - val_accuracy: 0.8570\n",
      "Epoch 185/312\n",
      "6/6 [==============================] - 1s 85ms/step - loss: 0.8565 - accuracy: 0.7834 - val_loss: 0.8941 - val_accuracy: 0.8550\n",
      "Epoch 186/312\n",
      "6/6 [==============================] - 1s 87ms/step - loss: 0.8547 - accuracy: 0.7873 - val_loss: 0.8931 - val_accuracy: 0.8540\n",
      "Epoch 187/312\n",
      "6/6 [==============================] - 1s 152ms/step - loss: 0.8545 - accuracy: 0.7853 - val_loss: 0.8945 - val_accuracy: 0.8540\n",
      "Epoch 188/312\n",
      "6/6 [==============================] - 1s 85ms/step - loss: 0.8532 - accuracy: 0.7783 - val_loss: 0.8917 - val_accuracy: 0.8580\n",
      "Epoch 189/312\n",
      "6/6 [==============================] - 1s 90ms/step - loss: 0.8510 - accuracy: 0.7835 - val_loss: 0.8923 - val_accuracy: 0.8560\n",
      "Epoch 190/312\n",
      "6/6 [==============================] - 0s 80ms/step - loss: 0.8507 - accuracy: 0.7908 - val_loss: 0.8914 - val_accuracy: 0.8580\n",
      "Epoch 191/312\n",
      "6/6 [==============================] - 1s 99ms/step - loss: 0.8492 - accuracy: 0.7915 - val_loss: 0.8923 - val_accuracy: 0.8560\n",
      "Epoch 192/312\n",
      "6/6 [==============================] - 1s 91ms/step - loss: 0.8477 - accuracy: 0.7797 - val_loss: 0.8879 - val_accuracy: 0.8560\n",
      "Epoch 193/312\n",
      "6/6 [==============================] - 1s 134ms/step - loss: 0.8476 - accuracy: 0.7776 - val_loss: 0.8841 - val_accuracy: 0.8550\n",
      "Epoch 194/312\n",
      "6/6 [==============================] - 1s 94ms/step - loss: 0.8470 - accuracy: 0.7816 - val_loss: 0.8858 - val_accuracy: 0.8580\n",
      "Epoch 195/312\n",
      "6/6 [==============================] - 1s 90ms/step - loss: 0.8458 - accuracy: 0.7813 - val_loss: 0.8881 - val_accuracy: 0.8580\n",
      "Epoch 196/312\n",
      "6/6 [==============================] - 1s 91ms/step - loss: 0.8445 - accuracy: 0.7884 - val_loss: 0.8870 - val_accuracy: 0.8570\n",
      "Epoch 197/312\n",
      "6/6 [==============================] - 0s 80ms/step - loss: 0.8429 - accuracy: 0.7867 - val_loss: 0.8856 - val_accuracy: 0.8550\n",
      "Epoch 198/312\n",
      "6/6 [==============================] - 1s 88ms/step - loss: 0.8419 - accuracy: 0.7841 - val_loss: 0.8868 - val_accuracy: 0.8540\n",
      "Epoch 199/312\n",
      "6/6 [==============================] - 1s 97ms/step - loss: 0.8416 - accuracy: 0.7929 - val_loss: 0.8858 - val_accuracy: 0.8550\n",
      "Epoch 200/312\n",
      "6/6 [==============================] - 1s 96ms/step - loss: 0.8407 - accuracy: 0.7814 - val_loss: 0.8805 - val_accuracy: 0.8560\n",
      "Epoch 201/312\n",
      "6/6 [==============================] - 1s 91ms/step - loss: 0.8402 - accuracy: 0.7776 - val_loss: 0.8786 - val_accuracy: 0.8570\n",
      "Epoch 202/312\n",
      "6/6 [==============================] - 1s 87ms/step - loss: 0.8394 - accuracy: 0.7881 - val_loss: 0.8835 - val_accuracy: 0.8580\n",
      "Epoch 203/312\n",
      "6/6 [==============================] - 1s 90ms/step - loss: 0.8381 - accuracy: 0.7900 - val_loss: 0.8802 - val_accuracy: 0.8580\n",
      "Epoch 204/312\n",
      "6/6 [==============================] - 1s 154ms/step - loss: 0.8369 - accuracy: 0.7813 - val_loss: 0.8796 - val_accuracy: 0.8590\n",
      "Epoch 205/312\n",
      "6/6 [==============================] - 1s 86ms/step - loss: 0.8354 - accuracy: 0.7878 - val_loss: 0.8806 - val_accuracy: 0.8590\n",
      "Epoch 206/312\n",
      "6/6 [==============================] - 1s 89ms/step - loss: 0.8348 - accuracy: 0.7915 - val_loss: 0.8812 - val_accuracy: 0.8520\n",
      "Epoch 207/312\n",
      "6/6 [==============================] - 1s 87ms/step - loss: 0.8333 - accuracy: 0.7837 - val_loss: 0.8770 - val_accuracy: 0.8560\n",
      "Epoch 208/312\n",
      "6/6 [==============================] - 1s 92ms/step - loss: 0.8326 - accuracy: 0.7795 - val_loss: 0.8744 - val_accuracy: 0.8570\n",
      "Epoch 209/312\n",
      "6/6 [==============================] - 0s 83ms/step - loss: 0.8327 - accuracy: 0.7827 - val_loss: 0.8744 - val_accuracy: 0.8600\n",
      "Epoch 210/312\n",
      "6/6 [==============================] - 1s 132ms/step - loss: 0.8317 - accuracy: 0.7916 - val_loss: 0.8730 - val_accuracy: 0.8610\n",
      "Epoch 211/312\n",
      "6/6 [==============================] - 1s 145ms/step - loss: 0.8301 - accuracy: 0.7848 - val_loss: 0.8702 - val_accuracy: 0.8560\n",
      "Epoch 212/312\n",
      "6/6 [==============================] - 1s 162ms/step - loss: 0.8300 - accuracy: 0.7814 - val_loss: 0.8711 - val_accuracy: 0.8570\n",
      "Epoch 213/312\n",
      "6/6 [==============================] - 1s 133ms/step - loss: 0.8287 - accuracy: 0.7857 - val_loss: 0.8710 - val_accuracy: 0.8540\n",
      "Epoch 214/312\n",
      "6/6 [==============================] - 1s 162ms/step - loss: 0.8273 - accuracy: 0.7848 - val_loss: 0.8759 - val_accuracy: 0.8580\n",
      "Epoch 215/312\n",
      "6/6 [==============================] - 2s 443ms/step - loss: 0.8270 - accuracy: 0.7878 - val_loss: 0.8804 - val_accuracy: 0.8550\n",
      "Epoch 216/312\n",
      "6/6 [==============================] - 2s 177ms/step - loss: 0.8269 - accuracy: 0.7918 - val_loss: 0.8761 - val_accuracy: 0.8590\n",
      "Epoch 217/312\n",
      "6/6 [==============================] - 1s 106ms/step - loss: 0.8252 - accuracy: 0.7859 - val_loss: 0.8722 - val_accuracy: 0.8590\n",
      "Epoch 218/312\n",
      "6/6 [==============================] - 1s 115ms/step - loss: 0.8245 - accuracy: 0.7859 - val_loss: 0.8712 - val_accuracy: 0.8530\n",
      "Epoch 219/312\n",
      "6/6 [==============================] - 2s 365ms/step - loss: 0.8242 - accuracy: 0.7899 - val_loss: 0.8719 - val_accuracy: 0.8560\n",
      "Epoch 220/312\n",
      "6/6 [==============================] - 2s 197ms/step - loss: 0.8232 - accuracy: 0.7878 - val_loss: 0.8713 - val_accuracy: 0.8600\n",
      "Epoch 221/312\n",
      "6/6 [==============================] - 1s 123ms/step - loss: 0.8224 - accuracy: 0.7824 - val_loss: 0.8682 - val_accuracy: 0.8550\n",
      "Epoch 222/312\n",
      "6/6 [==============================] - 1s 147ms/step - loss: 0.8215 - accuracy: 0.7865 - val_loss: 0.8667 - val_accuracy: 0.8600\n",
      "Epoch 223/312\n",
      "6/6 [==============================] - 1s 133ms/step - loss: 0.8207 - accuracy: 0.7919 - val_loss: 0.8668 - val_accuracy: 0.8590\n",
      "Epoch 224/312\n",
      "6/6 [==============================] - 1s 188ms/step - loss: 0.8207 - accuracy: 0.7824 - val_loss: 0.8721 - val_accuracy: 0.8610\n",
      "Epoch 225/312\n",
      "6/6 [==============================] - 1s 113ms/step - loss: 0.8197 - accuracy: 0.7824 - val_loss: 0.8702 - val_accuracy: 0.8590\n",
      "Epoch 226/312\n",
      "6/6 [==============================] - 1s 144ms/step - loss: 0.8184 - accuracy: 0.7894 - val_loss: 0.8682 - val_accuracy: 0.8590\n",
      "Epoch 227/312\n",
      "6/6 [==============================] - 1s 118ms/step - loss: 0.8177 - accuracy: 0.7859 - val_loss: 0.8664 - val_accuracy: 0.8600\n",
      "Epoch 228/312\n",
      "6/6 [==============================] - 1s 133ms/step - loss: 0.8173 - accuracy: 0.7835 - val_loss: 0.8647 - val_accuracy: 0.8580\n",
      "Epoch 229/312\n",
      "6/6 [==============================] - 1s 219ms/step - loss: 0.8168 - accuracy: 0.7846 - val_loss: 0.8641 - val_accuracy: 0.8590\n",
      "Epoch 230/312\n",
      "6/6 [==============================] - 1s 128ms/step - loss: 0.8159 - accuracy: 0.7870 - val_loss: 0.8610 - val_accuracy: 0.8620\n",
      "Epoch 231/312\n",
      "6/6 [==============================] - 1s 104ms/step - loss: 0.8156 - accuracy: 0.7919 - val_loss: 0.8608 - val_accuracy: 0.8550\n",
      "Epoch 232/312\n",
      "6/6 [==============================] - 1s 108ms/step - loss: 0.8146 - accuracy: 0.7891 - val_loss: 0.8606 - val_accuracy: 0.8530\n",
      "Epoch 233/312\n",
      "6/6 [==============================] - 1s 166ms/step - loss: 0.8137 - accuracy: 0.7895 - val_loss: 0.8563 - val_accuracy: 0.8610\n",
      "Epoch 234/312\n",
      "6/6 [==============================] - 1s 217ms/step - loss: 0.8130 - accuracy: 0.7900 - val_loss: 0.8558 - val_accuracy: 0.8530\n",
      "Epoch 235/312\n",
      "6/6 [==============================] - 1s 142ms/step - loss: 0.8129 - accuracy: 0.7884 - val_loss: 0.8571 - val_accuracy: 0.8600\n",
      "Epoch 236/312\n",
      "6/6 [==============================] - 1s 144ms/step - loss: 0.8123 - accuracy: 0.7953 - val_loss: 0.8601 - val_accuracy: 0.8520\n",
      "Epoch 237/312\n",
      "6/6 [==============================] - 1s 120ms/step - loss: 0.8117 - accuracy: 0.7956 - val_loss: 0.8552 - val_accuracy: 0.8580\n",
      "Epoch 238/312\n",
      "6/6 [==============================] - 1s 130ms/step - loss: 0.8108 - accuracy: 0.7861 - val_loss: 0.8539 - val_accuracy: 0.8540\n",
      "Epoch 239/312\n",
      "6/6 [==============================] - 1s 119ms/step - loss: 0.8102 - accuracy: 0.7854 - val_loss: 0.8563 - val_accuracy: 0.8580\n",
      "Epoch 240/312\n",
      "6/6 [==============================] - 1s 117ms/step - loss: 0.8093 - accuracy: 0.7881 - val_loss: 0.8608 - val_accuracy: 0.8510\n",
      "Epoch 241/312\n",
      "6/6 [==============================] - 1s 108ms/step - loss: 0.8095 - accuracy: 0.7940 - val_loss: 0.8577 - val_accuracy: 0.8550\n",
      "Epoch 242/312\n",
      "6/6 [==============================] - 1s 122ms/step - loss: 0.8078 - accuracy: 0.7913 - val_loss: 0.8538 - val_accuracy: 0.8540\n",
      "Epoch 243/312\n",
      "6/6 [==============================] - 1s 265ms/step - loss: 0.8078 - accuracy: 0.7776 - val_loss: 0.8513 - val_accuracy: 0.8570\n",
      "Epoch 244/312\n",
      "6/6 [==============================] - 1s 117ms/step - loss: 0.8073 - accuracy: 0.7841 - val_loss: 0.8591 - val_accuracy: 0.8580\n",
      "Epoch 245/312\n",
      "6/6 [==============================] - 1s 186ms/step - loss: 0.8067 - accuracy: 0.7878 - val_loss: 0.8579 - val_accuracy: 0.8520\n",
      "Epoch 246/312\n",
      "6/6 [==============================] - 1s 148ms/step - loss: 0.8067 - accuracy: 0.7857 - val_loss: 0.8566 - val_accuracy: 0.8550\n",
      "Epoch 247/312\n",
      "6/6 [==============================] - 1s 102ms/step - loss: 0.8054 - accuracy: 0.7935 - val_loss: 0.8518 - val_accuracy: 0.8580\n",
      "Epoch 248/312\n",
      "6/6 [==============================] - 1s 102ms/step - loss: 0.8033 - accuracy: 0.7899 - val_loss: 0.8482 - val_accuracy: 0.8610\n",
      "Epoch 249/312\n",
      "6/6 [==============================] - 1s 132ms/step - loss: 0.8035 - accuracy: 0.7843 - val_loss: 0.8425 - val_accuracy: 0.8580\n",
      "Epoch 250/312\n",
      "6/6 [==============================] - 1s 206ms/step - loss: 0.8029 - accuracy: 0.7865 - val_loss: 0.8412 - val_accuracy: 0.8590\n",
      "Epoch 251/312\n",
      "6/6 [==============================] - 1s 126ms/step - loss: 0.8025 - accuracy: 0.7814 - val_loss: 0.8450 - val_accuracy: 0.8550\n",
      "Epoch 252/312\n",
      "6/6 [==============================] - 1s 108ms/step - loss: 0.8027 - accuracy: 0.7824 - val_loss: 0.8505 - val_accuracy: 0.8560\n",
      "Epoch 253/312\n",
      "6/6 [==============================] - 1s 107ms/step - loss: 0.8017 - accuracy: 0.7899 - val_loss: 0.8496 - val_accuracy: 0.8570\n",
      "Epoch 254/312\n",
      "6/6 [==============================] - 1s 96ms/step - loss: 0.8004 - accuracy: 0.7951 - val_loss: 0.8536 - val_accuracy: 0.8570\n",
      "Epoch 255/312\n",
      "6/6 [==============================] - 1s 98ms/step - loss: 0.8003 - accuracy: 0.7967 - val_loss: 0.8524 - val_accuracy: 0.8570\n",
      "Epoch 256/312\n",
      "6/6 [==============================] - 1s 118ms/step - loss: 0.7990 - accuracy: 0.7878 - val_loss: 0.8508 - val_accuracy: 0.8600\n",
      "Epoch 257/312\n",
      "6/6 [==============================] - 1s 197ms/step - loss: 0.7987 - accuracy: 0.7840 - val_loss: 0.8456 - val_accuracy: 0.8590\n",
      "Epoch 258/312\n",
      "6/6 [==============================] - 1s 144ms/step - loss: 0.7980 - accuracy: 0.7938 - val_loss: 0.8417 - val_accuracy: 0.8630\n",
      "Epoch 259/312\n",
      "6/6 [==============================] - 1s 258ms/step - loss: 0.7973 - accuracy: 0.7949 - val_loss: 0.8430 - val_accuracy: 0.8600\n",
      "Epoch 260/312\n",
      "6/6 [==============================] - 1s 210ms/step - loss: 0.7967 - accuracy: 0.7891 - val_loss: 0.8451 - val_accuracy: 0.8570\n",
      "Epoch 261/312\n",
      "6/6 [==============================] - 1s 143ms/step - loss: 0.7968 - accuracy: 0.7840 - val_loss: 0.8452 - val_accuracy: 0.8540\n",
      "Epoch 262/312\n",
      "6/6 [==============================] - 1s 254ms/step - loss: 0.7961 - accuracy: 0.7854 - val_loss: 0.8411 - val_accuracy: 0.8570\n",
      "Epoch 263/312\n",
      "6/6 [==============================] - 1s 126ms/step - loss: 0.7958 - accuracy: 0.7935 - val_loss: 0.8440 - val_accuracy: 0.8600\n",
      "Epoch 264/312\n",
      "6/6 [==============================] - 1s 194ms/step - loss: 0.7947 - accuracy: 0.7880 - val_loss: 0.8461 - val_accuracy: 0.8560\n",
      "Epoch 265/312\n",
      "6/6 [==============================] - 1s 124ms/step - loss: 0.7949 - accuracy: 0.7851 - val_loss: 0.8455 - val_accuracy: 0.8550\n",
      "Epoch 266/312\n",
      "6/6 [==============================] - 1s 173ms/step - loss: 0.7940 - accuracy: 0.7973 - val_loss: 0.8424 - val_accuracy: 0.8610\n",
      "Epoch 267/312\n",
      "6/6 [==============================] - 1s 187ms/step - loss: 0.7935 - accuracy: 0.7889 - val_loss: 0.8441 - val_accuracy: 0.8570\n",
      "Epoch 268/312\n",
      "6/6 [==============================] - 1s 120ms/step - loss: 0.7928 - accuracy: 0.7881 - val_loss: 0.8440 - val_accuracy: 0.8570\n",
      "Epoch 269/312\n",
      "6/6 [==============================] - 1s 131ms/step - loss: 0.7923 - accuracy: 0.7953 - val_loss: 0.8444 - val_accuracy: 0.8590\n",
      "Epoch 270/312\n",
      "6/6 [==============================] - 1s 129ms/step - loss: 0.7923 - accuracy: 0.7883 - val_loss: 0.8431 - val_accuracy: 0.8550\n",
      "Epoch 271/312\n",
      "6/6 [==============================] - 1s 127ms/step - loss: 0.7918 - accuracy: 0.7889 - val_loss: 0.8377 - val_accuracy: 0.8600\n",
      "Epoch 272/312\n",
      "6/6 [==============================] - 1s 222ms/step - loss: 0.7915 - accuracy: 0.7937 - val_loss: 0.8405 - val_accuracy: 0.8560\n",
      "Epoch 273/312\n",
      "6/6 [==============================] - 1s 116ms/step - loss: 0.7907 - accuracy: 0.7916 - val_loss: 0.8386 - val_accuracy: 0.8590\n",
      "Epoch 274/312\n",
      "6/6 [==============================] - 1s 140ms/step - loss: 0.7895 - accuracy: 0.7889 - val_loss: 0.8393 - val_accuracy: 0.8550\n",
      "Epoch 275/312\n",
      "6/6 [==============================] - 1s 146ms/step - loss: 0.7901 - accuracy: 0.7903 - val_loss: 0.8372 - val_accuracy: 0.8580\n",
      "Epoch 276/312\n",
      "6/6 [==============================] - 1s 118ms/step - loss: 0.7901 - accuracy: 0.7983 - val_loss: 0.8333 - val_accuracy: 0.8570\n",
      "Epoch 277/312\n",
      "6/6 [==============================] - 1s 163ms/step - loss: 0.7885 - accuracy: 0.7949 - val_loss: 0.8333 - val_accuracy: 0.8590\n",
      "Epoch 278/312\n",
      "6/6 [==============================] - 1s 125ms/step - loss: 0.7878 - accuracy: 0.7873 - val_loss: 0.8323 - val_accuracy: 0.8600\n",
      "Epoch 279/312\n",
      "6/6 [==============================] - 1s 162ms/step - loss: 0.7873 - accuracy: 0.7878 - val_loss: 0.8326 - val_accuracy: 0.8590\n",
      "Epoch 280/312\n",
      "6/6 [==============================] - 1s 112ms/step - loss: 0.7867 - accuracy: 0.7916 - val_loss: 0.8329 - val_accuracy: 0.8600\n",
      "Epoch 281/312\n",
      "6/6 [==============================] - 1s 116ms/step - loss: 0.7861 - accuracy: 0.7946 - val_loss: 0.8323 - val_accuracy: 0.8570\n",
      "Epoch 282/312\n",
      "6/6 [==============================] - 1s 117ms/step - loss: 0.7860 - accuracy: 0.7996 - val_loss: 0.8299 - val_accuracy: 0.8580\n",
      "Epoch 283/312\n",
      "6/6 [==============================] - 1s 123ms/step - loss: 0.7861 - accuracy: 0.7940 - val_loss: 0.8296 - val_accuracy: 0.8570\n",
      "Epoch 284/312\n",
      "6/6 [==============================] - 1s 231ms/step - loss: 0.7851 - accuracy: 0.7915 - val_loss: 0.8358 - val_accuracy: 0.8500\n",
      "Epoch 285/312\n",
      "6/6 [==============================] - 1s 237ms/step - loss: 0.7861 - accuracy: 0.7943 - val_loss: 0.8332 - val_accuracy: 0.8610\n",
      "Epoch 286/312\n",
      "6/6 [==============================] - 1s 142ms/step - loss: 0.7839 - accuracy: 0.7899 - val_loss: 0.8283 - val_accuracy: 0.8610\n",
      "Epoch 287/312\n",
      "6/6 [==============================] - 1s 141ms/step - loss: 0.7838 - accuracy: 0.7900 - val_loss: 0.8241 - val_accuracy: 0.8590\n",
      "Epoch 288/312\n",
      "6/6 [==============================] - 1s 149ms/step - loss: 0.7832 - accuracy: 0.7949 - val_loss: 0.8241 - val_accuracy: 0.8620\n",
      "Epoch 289/312\n",
      "6/6 [==============================] - 1s 128ms/step - loss: 0.7826 - accuracy: 0.7943 - val_loss: 0.8258 - val_accuracy: 0.8590\n",
      "Epoch 290/312\n",
      "6/6 [==============================] - 1s 174ms/step - loss: 0.7828 - accuracy: 0.7870 - val_loss: 0.8295 - val_accuracy: 0.8610\n",
      "Epoch 291/312\n",
      "6/6 [==============================] - 1s 140ms/step - loss: 0.7816 - accuracy: 0.7922 - val_loss: 0.8281 - val_accuracy: 0.8540\n",
      "Epoch 292/312\n",
      "6/6 [==============================] - 1s 119ms/step - loss: 0.7822 - accuracy: 0.8000 - val_loss: 0.8259 - val_accuracy: 0.8600\n",
      "Epoch 293/312\n",
      "6/6 [==============================] - 1s 114ms/step - loss: 0.7808 - accuracy: 0.7895 - val_loss: 0.8281 - val_accuracy: 0.8580\n",
      "Epoch 294/312\n",
      "6/6 [==============================] - 1s 122ms/step - loss: 0.7816 - accuracy: 0.7781 - val_loss: 0.8249 - val_accuracy: 0.8580\n",
      "Epoch 295/312\n",
      "6/6 [==============================] - 1s 199ms/step - loss: 0.7800 - accuracy: 0.7918 - val_loss: 0.8232 - val_accuracy: 0.8620\n",
      "Epoch 296/312\n",
      "6/6 [==============================] - 1s 107ms/step - loss: 0.7800 - accuracy: 0.8015 - val_loss: 0.8231 - val_accuracy: 0.8600\n",
      "Epoch 297/312\n",
      "6/6 [==============================] - 1s 111ms/step - loss: 0.7783 - accuracy: 0.7969 - val_loss: 0.8286 - val_accuracy: 0.8580\n",
      "Epoch 298/312\n",
      "6/6 [==============================] - 1s 116ms/step - loss: 0.7788 - accuracy: 0.7883 - val_loss: 0.8240 - val_accuracy: 0.8590\n",
      "Epoch 299/312\n",
      "6/6 [==============================] - 1s 121ms/step - loss: 0.7778 - accuracy: 0.7935 - val_loss: 0.8227 - val_accuracy: 0.8540\n",
      "Epoch 300/312\n",
      "6/6 [==============================] - 1s 187ms/step - loss: 0.7781 - accuracy: 0.7980 - val_loss: 0.8241 - val_accuracy: 0.8600\n",
      "Epoch 301/312\n",
      "6/6 [==============================] - 1s 102ms/step - loss: 0.7776 - accuracy: 0.7943 - val_loss: 0.8262 - val_accuracy: 0.8600\n",
      "Epoch 302/312\n",
      "6/6 [==============================] - 1s 110ms/step - loss: 0.7768 - accuracy: 0.7886 - val_loss: 0.8212 - val_accuracy: 0.8620\n",
      "Epoch 303/312\n",
      "6/6 [==============================] - 1s 154ms/step - loss: 0.7764 - accuracy: 0.7903 - val_loss: 0.8236 - val_accuracy: 0.8590\n",
      "Epoch 304/312\n",
      "6/6 [==============================] - 1s 143ms/step - loss: 0.7764 - accuracy: 0.7951 - val_loss: 0.8220 - val_accuracy: 0.8610\n",
      "Epoch 305/312\n",
      "6/6 [==============================] - 1s 194ms/step - loss: 0.7749 - accuracy: 0.7956 - val_loss: 0.8215 - val_accuracy: 0.8600\n",
      "Epoch 306/312\n",
      "6/6 [==============================] - 1s 122ms/step - loss: 0.7756 - accuracy: 0.7881 - val_loss: 0.8167 - val_accuracy: 0.8600\n",
      "Epoch 307/312\n",
      "6/6 [==============================] - 1s 104ms/step - loss: 0.7764 - accuracy: 0.7821 - val_loss: 0.8185 - val_accuracy: 0.8560\n",
      "Epoch 308/312\n",
      "6/6 [==============================] - 1s 115ms/step - loss: 0.7762 - accuracy: 0.7940 - val_loss: 0.8197 - val_accuracy: 0.8570\n",
      "Epoch 309/312\n",
      "6/6 [==============================] - 1s 167ms/step - loss: 0.7751 - accuracy: 0.7976 - val_loss: 0.8220 - val_accuracy: 0.8590\n",
      "Epoch 310/312\n",
      "6/6 [==============================] - 1s 116ms/step - loss: 0.7735 - accuracy: 0.7922 - val_loss: 0.8175 - val_accuracy: 0.8590\n",
      "Epoch 311/312\n",
      "6/6 [==============================] - 1s 107ms/step - loss: 0.7729 - accuracy: 0.7992 - val_loss: 0.8159 - val_accuracy: 0.8610\n",
      "Epoch 312/312\n",
      "6/6 [==============================] - 1s 111ms/step - loss: 0.7729 - accuracy: 0.8000 - val_loss: 0.8162 - val_accuracy: 0.8580\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAHFCAYAAAAHcXhbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAABNMklEQVR4nO3deXxU1f3/8dedSTLZVwghEBZZRFYXBHGpIC6gIEhpUamCtu6g1GrVqnVpFZevShWXLipYFa1VKb9aRZRFFFEUEFBkkyUsIUDInkySmfP7Y5IhIazJZO5M8n4+HveRmXvv3PnMYXzM23POvdcyxhhEREREwpTD7gJEREREGkNhRkRERMKawoyIiIiENYUZERERCWsKMyIiIhLWFGZEREQkrCnMiIiISFhTmBEREZGwpjAjIiIiYU1hRkTqmTFjBpZlYVkWCxcurLfdGEPXrl2xLIvBgwcH9L0ty+LBBx887tdt2bIFy7KYMWNGQPYTkfChMCMih5WQkMDLL79cb/2iRYvYtGkTCQkJNlQlIlKXwoyIHNa4ceN49913KSwsrLP+5ZdfZtCgQXTo0MGmykREDlCYEZHDuuKKKwCYNWuWf11BQQHvvvsu11577SFfk5eXx80330y7du2IiorihBNO4N5778XtdtfZr7CwkOuuu460tDTi4+MZNmwY69evP+QxN2zYwJVXXkl6ejoul4uTTjqJ559/PkCf0ufzzz9n6NChJCQkEBsby5lnnskHH3xQZ5/S0lLuuOMOOnfuTHR0NKmpqfTv379O+/z0009cfvnlZGZm4nK5aNOmDUOHDmXlypUBrVdEDoiwuwARCV2JiYmMHTuWV155hRtuuAHwBRuHw8G4ceOYNm1anf3Ly8sZMmQImzZt4qGHHqJv374sXryYqVOnsnLlSn84MMYwevRolixZwh//+EdOP/10vvjiC4YPH16vhh9++IEzzzyTDh068NRTT5GRkcHcuXO59dZb2bt3Lw888ECjP+eiRYu44IIL6Nu3Ly+//DIul4sXXniBkSNHMmvWLMaNGwfA7bffzj//+U/+/Oc/c8opp1BSUsKaNWvYt2+f/1gXX3wxHo+HJ554gg4dOrB3716WLFlCfn5+o+sUkcMwIiIHefXVVw1gli1bZhYsWGAAs2bNGmOMMaeffrqZOHGiMcaYXr16mXPPPdf/updeeskA5l//+led4z3++OMGMB9//LExxpgPP/zQAOYvf/lLnf0eeeQRA5gHHnjAv+6iiy4y7du3NwUFBXX2nTRpkomOjjZ5eXnGGGM2b95sAPPqq68e8bMdar8zzjjDpKenm6KiIv+6qqoq07t3b9O+fXvj9XqNMcb07t3bjB49+rDH3rt3rwHMtGnTjliDiASWhplE5IjOPfdcunTpwiuvvMLq1atZtmzZYYeY5s+fT1xcHGPHjq2zfuLEiQB8+umnACxYsACA8ePH19nvyiuvrPO8vLycTz/9lMsuu4zY2Fiqqqr8y8UXX0x5eTlLly5t1OcrKSnhq6++YuzYscTHx/vXO51OrrrqKrZv3866desAGDBgAB9++CF33303CxcupKysrM6xUlNT6dKlC08++SRPP/00K1aswOv1Nqo+ETk6hRkROSLLsrjmmmt4/fXXeemll+jevTvnnHPOIffdt28fGRkZWJZVZ316ejoRERH+4Zh9+/YRERFBWlpanf0yMjLqHa+qqornnnuOyMjIOsvFF18MwN69exv1+fbv348xhrZt29bblpmZ6a8D4Nlnn+Wuu+5i9uzZDBkyhNTUVEaPHs2GDRsAX1t9+umnXHTRRTzxxBOceuqptG7dmltvvZWioqJG1Skih6cwIyJHNXHiRPbu3ctLL73ENddcc9j90tLS2L17N8aYOutzc3OpqqqiVatW/v2qqqrqzDUByMnJqfM8JSUFp9PJxIkTWbZs2SGXmlDTUCkpKTgcDnbt2lVv286dOwH8dcfFxfHQQw/x448/kpOTw4svvsjSpUsZOXKk/zUdO3bk5ZdfJicnh3Xr1vHb3/6WF154gTvvvLNRdYrI4SnMiMhRtWvXjjvvvJORI0cyYcKEw+43dOhQiouLmT17dp31r732mn87wJAhQwB444036uz35ptv1nkeGxvLkCFDWLFiBX379qV///71loN7d45XXFwcAwcO5L333qszbOT1enn99ddp37493bt3r/e6Nm3aMHHiRK644grWrVtHaWlpvX26d+/OfffdR58+fVi+fHmj6hSRw9PZTCJyTB577LGj7nP11Vfz/PPPM2HCBLZs2UKfPn34/PPPefTRR7n44os5//zzAbjwwgv52c9+xu9//3tKSkro378/X3zxBf/85z/rHfMvf/kLZ599Nueccw433XQTnTp1oqioiI0bN/L//t//Y/78+Y3+bFOnTuWCCy5gyJAh3HHHHURFRfHCCy+wZs0aZs2a5R82GzhwICNGjKBv376kpKSwdu1a/vnPfzJo0CBiY2NZtWoVkyZN4he/+AXdunUjKiqK+fPns2rVKu6+++5G1ykih6YwIyIBEx0dzYIFC7j33nt58skn2bNnD+3ateOOO+6ocwq1w+Fgzpw53H777TzxxBNUVFRw1lln8b///Y8ePXrUOWbPnj1Zvnw5f/rTn7jvvvvIzc0lOTmZbt26NXqIqca5557L/PnzeeCBB5g4cSJer5d+/foxZ84cRowY4d/vvPPOY86cOTzzzDOUlpbSrl07rr76au69917AN+enS5cuvPDCC2RnZ2NZFieccAJPPfUUkydPDkitIlKfZQ4e3BYREREJI5ozIyIiImFNYUZERETCmsKMiIiIhDWFGREREQlrCjMiIiIS1hRmREREJKw1++vMeL1edu7cSUJCQr37xYiIiEhoMsZQVFREZmYmDseR+16afZjZuXMnWVlZdpchIiIiDZCdnU379u2PuE+zDzMJCQmArzESExNtrkZERESORWFhIVlZWf7f8SNp9mGmZmgpMTFRYUZERCTMHMsUEU0AFhERkbCmMCMiIiJhTWFGREREwlqznzMjIiLNh8fjobKy0u4yJAAiIyNxOp0BOZbCjIiIhDxjDDk5OeTn59tdigRQcnIyGRkZjb4OnMKMiIiEvJogk56eTmxsrC6CGuaMMZSWlpKbmwtA27ZtG3U8hRkREQlpHo/HH2TS0tLsLkcCJCYmBoDc3FzS09MbNeSkCcAiIhLSaubIxMbG2lyJBFrNv2lj50EpzIiISFjQ0FLzE6h/U1vDzGeffcbIkSPJzMzEsixmz5592H1vuOEGLMti2rRpQatPREREQp+tYaakpIR+/foxffr0I+43e/ZsvvrqKzIzM4NUmYiISGgaPHgwU6ZMsbuMkGLrBODhw4czfPjwI+6zY8cOJk2axNy5c7nkkkuCVJmIiEjjHG0IZcKECcyYMeO4j/vee+8RGRnZwKqap5A+m8nr9XLVVVdx55130qtXr2N6jdvtxu12+58XFhY2SW1lFR7ySiuIcjponeBqkvcQEZHwtWvXLv/jt99+mz/+8Y+sW7fOv67mbJ4alZWVxxRSUlNTA1dkMxHSE4Aff/xxIiIiuPXWW4/5NVOnTiUpKcm/ZGVlNUltf/1sE2c9Np9pn6xvkuOLiEh4y8jI8C9JSUlYluV/Xl5eTnJyMv/6178YPHgw0dHRvP766+zbt48rrriC9u3bExsbS58+fZg1a1ad4x48zNSpUyceffRRrr32WhISEujQoQN/+9vfgvxp7RWyYebbb7/lL3/5CzNmzDiu2c733HMPBQUF/iU7O7tJ6kuI9qXnovKqJjm+iIgcnjGG0ooqWxZjTMA+x1133cWtt97K2rVrueiiiygvL+e0007jv//9L2vWrOH666/nqquu4quvvjricZ566in69+/PihUruPnmm7npppv48ccfA1ZnqAvZYabFixeTm5tLhw4d/Os8Hg+/+93vmDZtGlu2bDnk61wuFy5X0w/7JLh8TVfsVpgREQm2skoPPf8415b3/uHhi4iNCszP55QpUxgzZkyddXfccYf/8eTJk/noo4945513GDhw4GGPc/HFF3PzzTcDvoD0zDPPsHDhQnr06BGQOkNdyIaZq666ivPPP7/OuosuuoirrrqKa665xqaqDoiPrg4z6pkREZEG6t+/f53nHo+Hxx57jLfffpsdO3b454HGxcUd8Th9+/b1P64Zzqq5VUBLYGuYKS4uZuPGjf7nmzdvZuXKlaSmptKhQ4d6l62OjIwkIyODE088Mdil1hNf3TNTpJ4ZEZGgi4l08sPDF9n23oFycEh56qmneOaZZ5g2bRp9+vQhLi6OKVOmUFFRccTjHDxx2LIsvF5vwOoMdbaGmW+++YYhQ4b4n99+++1Aw09XC6aE6p6ZonLdil5EJNgsywrYUE8oWbx4MaNGjeJXv/oV4Durd8OGDZx00kk2VxbabP0mDB48+LgmUh1unowdasKM5syIiEigdO3alXfffZclS5aQkpLC008/TU5OjsLMUYTs2UyhLt7l69IrLg/szHYREWm57r//fk499VQuuugiBg8eTEZGBqNHj7a7rJBnmWb+S1xYWEhSUhIFBQUkJiYG7LjF7ip6P+CbSb/24WHERAVuDFVERA4oLy9n8+bNdO7cmejoaLvLkQA60r/t8fx+q2emgeKinNRc/qbIrXkzIiIidlGYaSDLsvxnNOn0bBEREfsozDSCLpwnIiJiP4WZRtAtDUREROynMNMI8f5rzSjMiIiI2EVhphHiNcwkIiJiO4WZRjhwfyadzSQiImIXhZlGSNQwk4iIiO0UZhpBw0wiIiL2U5hpqF2rGJT3HwY5vteds0VEpEkMHjyYKVOm+J936tSJadOmHfE1lmUxe/bsRr93oI4TDAozDbV+LudteoxRji80zCQiIvWMHDmS888//5DbvvzySyzLYvny5cd1zGXLlnH99dcHojy/Bx98kJNPPrne+l27djF8+PCAvldTUZhpqNgUAFKsYk0AFhGRen79618zf/58tm7dWm/bK6+8wsknn8ypp556XMds3bo1sbGxgSrxiDIyMnC5XEF5r8ZSmGmo2DQAkq1izZkREZF6RowYQXp6OjNmzKizvrS0lLfffpvRo0dzxRVX0L59e2JjY+nTpw+zZs064jEPHmbasGEDP/vZz4iOjqZnz57Mmzev3mvuuusuunfvTmxsLCeccAL3338/lZW+/wmfMWMGDz30EN999x2WZWFZlr/eg4eZVq9ezXnnnUdMTAxpaWlcf/31FBcX+7dPnDiR0aNH83//93+0bduWtLQ0brnlFv97NaWIJn+H5iomFYBUijTMJCISbMZAZak97x0Zi/9Ow0cQERHB1VdfzYwZM/jjH/+IVf2ad955h4qKCn7zm98wa9Ys7rrrLhITE/nggw+46qqrOOGEExg4cOBRj+/1ehkzZgytWrVi6dKlFBYW1plfUyMhIYEZM2aQmZnJ6tWrue6660hISOD3v/8948aNY82aNXz00Ud88sknACQlJdU7RmlpKcOGDeOMM85g2bJl5Obm8pvf/IZJkybVCWsLFiygbdu2LFiwgI0bNzJu3DhOPvlkrrvuuqN+nsZQmGmoWF+YSbYUZkREgq6yFB7NtOe9/7ATouKOaddrr72WJ598koULFzJkyBDAN8Q0ZswY2rVrxx133OHfd/LkyXz00Ue88847xxRmPvnkE9auXcuWLVto3749AI8++mi9eS733Xef/3GnTp343e9+x9tvv83vf/97YmJiiI+PJyIigoyMjMO+1xtvvEFZWRmvvfYacXG+zz59+nRGjhzJ448/Tps2bQBISUlh+vTpOJ1OevTowSWXXMKnn36qMBOyqntmkinRnBkRETmkHj16cOaZZ/LKK68wZMgQNm3axOLFi/n444/xeDw89thjvP322+zYsQO3243b7faHhaNZu3YtHTp08AcZgEGDBtXb79///jfTpk1j48aNFBcXU1VVRWJi4nF9jrVr19KvX786tZ111ll4vV7WrVvnDzO9evXC6XT692nbti2rV68+rvdqCIWZhqrumYm0PFBRhDHG34UoIiJNLDLW10Ni13sfh1//+tdMmjSJ559/nldffZWOHTsydOhQnnzySZ555hmmTZtGnz59iIuLY8qUKVRUVBzTcY0x9dYd/Du0dOlSLr/8ch566CEuuugikpKSeOutt3jqqaeO6zMc6Teu9vrIyMh627xe73G9V0MozDRUZAwmMharspQkU0hZpYfYKDWniEhQWNYxD/XY7Ze//CW33XYbb775JjNnzuS6667DsiwWL17MqFGj+NWvfgX45sBs2LCBk0466ZiO27NnT7Zt28bOnTvJzPQNuX355Zd19vniiy/o2LEj9957r3/dwWdXRUVF4fF4jvpeM2fOpKSkxN8788UXX+BwOOjevfsx1duUdDZTY1QPNaVQRH6phppERKS++Ph4xo0bxx/+8Ad27tzJxIkTAejatSvz5s1jyZIlrF27lhtuuIGcnJxjPu7555/PiSeeyNVXX813333H4sWL64SWmvfYtm0bb731Fps2beLZZ5/l/fffr7NPp06d2Lx5MytXrmTv3r243e567zV+/Hiio6OZMGECa9asYcGCBUyePJmrrrrKP8RkJ4WZRrBqXWsmr+TYugVFRKTl+fWvf83+/fs5//zz6dChAwD3338/p556KhdddBGDBw8mIyOD0aNHH/MxHQ4H77//Pm63mwEDBvCb3/yGRx55pM4+o0aN4re//S2TJk3i5JNPZsmSJdx///119vn5z3/OsGHDGDJkCK1btz7k6eGxsbHMnTuXvLw8Tj/9dMaOHcvQoUOZPn368TdGE7DMoQbdmpHCwkKSkpIoKCg47glPRzXzUti8iCkVNzNm4u38rHvrwB5fREQoLy9n8+bNdO7cmejoaLvLkQA60r/t8fx+q2emMaovnJdqFbG/VD0zIiIidlCYaYxa15rZV6wwIyIiYgeFmcbwTwAuVs+MiIiITRRmGqO6ZybFKmafJgCLiIjYQmGmMarnzKRQxH6FGRGRJtXMz1dpkQL1b6ow0xgxB3pmdGq2iEjTqLmqbGmpTTeWlCZT82968JWDj5cuWdsY1deZSbaKFGZERJqI0+kkOTmZ3NxcwHfNE90+JrwZYygtLSU3N5fk5OQ693NqCIWZxtAEYBGRoKi5o3NNoJHmITk5+Yh36z5WCjONUT0BOMaqoKy0GK/X4HDo/xZERALNsizatm1Leno6lZW6fUxzEBkZ2egemRoKM43hSsQ4o7A8FSR7CygsryQ5NsruqkREmi2n0xmwH0BpPjQBuDEsCyvOdwuDNKtQp2eLiIjYQGGmseJaAb4wo9OzRUREgk9hprGqe2ZaWQXqmREREbGBwkxjxaUD0Ar1zIiIiNhBYaax/MNM6pkRERGxg8JMY9WeAKw7Z4uIiASdwkxjxdcMMxWwt9htczEiIiItj61h5rPPPmPkyJFkZmZiWRazZ8/2b6usrOSuu+6iT58+xMXFkZmZydVXX83OnTvtK/hQqoeZWlmFCjMiIiI2sDXMlJSU0K9fP6ZPn15vW2lpKcuXL+f+++9n+fLlvPfee6xfv55LL73UhkqPoNYwk8KMiIhI8Nl6BeDhw4czfPjwQ25LSkpi3rx5ddY999xzDBgwgG3bttGhQ4dglHh01WczpVLI3sIym4sRERFpecLqdgYFBQVYlkVycvJh93G73bjdB3pICgsLm7ao2DQAIiwvpmw/lR4vkU5NRRIREQmWsPnVLS8v5+677+bKK68kMTHxsPtNnTqVpKQk/5KVldW0hUVEYaKTAd9QU55OzxYREQmqsAgzlZWVXH755Xi9Xl544YUj7nvPPfdQUFDgX7Kzs5u8PqvmjCarkD1FmjcjIiISTCE/zFRZWckvf/lLNm/ezPz584/YKwPgcrlwuVxBqq5aXGvYu55WFLBHk4BFRESCKqTDTE2Q2bBhAwsWLCAtLc3ukg6t1s0m96pnRkREJKhsDTPFxcVs3LjR/3zz5s2sXLmS1NRUMjMzGTt2LMuXL+e///0vHo+HnJwcAFJTU4mKirKr7Ppq7s9kqWdGREQk2GwNM9988w1DhgzxP7/99tsBmDBhAg8++CBz5swB4OSTT67zugULFjB48OBglXl08W0ASCefdUWaACwiIhJMtoaZwYMHY4w57PYjbQspCb4w09rK5wv1zIiIiARVWJzNFPLiMwBIt/J1NpOIiEiQKcwEQvWp2elWvm5pICIiEmQKM4GQ4OuZSaOAvKJSm4sRERFpWRRmAiGuNcZy4LQMjrJ9uKs8dlckIiLSYijMBILD6b97tubNiIiIBJfCTIBYNadnW/nkKsyIiIgEjcJMoCQcOKMpt7Dc5mJERERaDoWZQPFfOG8/uwvVMyMiIhIsCjOBEl9z4bwCdqtnRkREJGgUZgKl1jCTemZERESCR2EmUPwTgPeTW6SeGRERkWBRmAmUOj0zCjMiIiLBojATKDVzZshnd4HCjIiISLAozARKQlsAXFYVzvI8yit1FWAREZFgUJgJlIgoTPVVgDOsPF0FWEREJEgUZgLIqu6daWPt17wZERGRIFGYCaTEdgC0tfJ0eraIiEiQKMwEUmJNz0weuwrKbC5GRESkZVCYCaTETADakscundEkIiISFAozgZTgCzMZ6pkREREJGoWZQEo8EGZ25qtnRkREJBgUZgLJH2b2q2dGREQkSBRmAqk6zCRapRQXFVBR5bW5IBERkeZPYSaQXAmYqAQA2pCna82IiIgEgcJMgFmJtScBK8yIiIg0NYWZQKu+1kxb8tiZr3kzIiIiTU1hJtAS2wPQ1trHTk0CFhERaXIKM4GWnAVAO2svu3R6toiISJNTmAm0pANhRsNMIiIiTU9hJtBq9czs1ARgERGRJqcwE2hJtYeZSm0uRkREpPlTmAm0xHYYLKKtSiLK9lFaUWV3RSIiIs2awkygRURhJfhOz25n7dE9mkRERJqYwkxTqH1Gk07PFhERaVIKM00hSadni4iIBIvCTFOo1TOzQ6dni4iINCmFmaaQpGEmERGRYFGYaQrJHQBob+3VzSZFRESamMJMU0jSMJOIiEiwKMw0heo5M4lWKUX5eRhjbC5IRESk+bI1zHz22WeMHDmSzMxMLMti9uzZdbYbY3jwwQfJzMwkJiaGwYMH8/3339tT7PGIisPEpAKQVrWbgrJKmwsSERFpvmwNMyUlJfTr14/p06cfcvsTTzzB008/zfTp01m2bBkZGRlccMEFFBUVBbnS42f5z2jShfNERESaUoSdbz58+HCGDx9+yG3GGKZNm8a9997LmDFjAJg5cyZt2rThzTff5IYbbghmqccvKQt2feefN9MzM9HuikRERJqlkJ0zs3nzZnJycrjwwgv961wuF+eeey5Lliw57OvcbjeFhYV1FltUn9HUztrL9v264aSIiEhTCdkwk5OTA0CbNm3qrG/Tpo1/26FMnTqVpKQk/5KVldWkdR5WrTOatuUpzIiIiDSVkA0zNSzLqvPcGFNvXW333HMPBQUF/iU7O7upSzy06jkz7a29ZCvMiIiINBlb58wcSUZGBuDroWnbtq1/fW5ubr3emtpcLhcul6vJ6zuqWj0z2Xm61oyIiEhTCdmemc6dO5ORkcG8efP86yoqKli0aBFnnnmmjZUdo+o5M62tAnLy8nWtGRERkSZia89McXExGzdu9D/fvHkzK1euJDU1lQ4dOjBlyhQeffRRunXrRrdu3Xj00UeJjY3lyiuvtLHqYxSTgomMw6osIbUql73FFbROCIEeIxERkWbG1jDzzTffMGTIEP/z22+/HYAJEyYwY8YMfv/731NWVsbNN9/M/v37GThwIB9//DEJCQl2lXzsLMt3rZk9P9Le2sO2vFKFGRERkSZgmWY+/lFYWEhSUhIFBQUkJgb5Wi9v/BI2zOUPlb9m4C9+x6iT2wX3/UVERMLU8fx+h+ycmWYh9QQAOlo5bNunM5pERESagsJMU0rtDEBHK1fXmhEREWkiCjNNKaUmzOxWmBEREWkiCjNNqbpnpoO1m+0KMyIiIk1CYaYpJXfAWA7iLDcVhTlUVHntrkhERKTZUZhpShEuSPSdwdSB3ezI15WARUREAk1hpolZqZo3IyIi0pQUZppazenZDoUZERGRpqAw09RqndGkScAiIiKBpzDT1HStGRERkSalMNPU/D0zOQozIiIiTUBhpqlV98ykWsXk5e21uRgREZHmR2GmqbkSMLGtAUh176CgtNLmgkRERJoXhZkgqH169ta8EpurERERaV4UZoKh+vTsTtZuNu9VmBEREQkkhZlgqHWPJoUZERGRwFKYCYaUA6dnK8yIiIgElsJMMNTMmXHkKMyIiIgEmMJMMFTPmclgPzv27scYY3NBIiIizYfCTDDEpmGi4nFYhhT3TvaVVNhdkYiISLOhMBMMloWV1gWAzpaGmkRERAJJYSZY0roB0MXayeY9CjMiIiKBojATLK18YeYEaxc/qWdGREQkYBRmgqUmzDh2sXlvsc3FiIiINB8KM8FSe5hJPTMiIiIBozATLNUTgFOsYgr25eD16vRsERGRQFCYCZaoOExiewDae3aws6DM5oJERESaB4WZILLqzJvRUJOIiEggKMwEUyvNmxEREQk0hZlg8k8C3sVPutaMiIhIQCjMBJP/WjPqmREREQkUhZlgqg4zHaxctu0psLkYERGR5kFhJpgSMjERMURaHhwFW3BXeeyuSEREJOwpzASTwwGtugLQmV1s3Vdqc0EiIiLhT2EmyKxW3QHfGU0bc3VbAxERkcZSmAm2tAM3nNywW2FGRESksRRmgs1/4bydbMgtsrkYERGR8KcwE2ytDvTMaJhJRESk8RRmgi3NNwE4zSpi/55dVHm8NhckIiIS3hRmgi0qDpOUBUBHbzbb8nRGk4iISGOEdJipqqrivvvuo3PnzsTExHDCCSfw8MMP4/WGd2+GlX4SAN0d29mgoSYREZFGibC7gCN5/PHHeemll5g5cya9evXim2++4ZprriEpKYnbbrvN7vIaLv0k2PAx3a3tbMwt5qJedhckIiISvkI6zHz55ZeMGjWKSy65BIBOnToxa9YsvvnmG5sra6T0ngCc6Mhm1m6d0SQiItIYIT3MdPbZZ/Ppp5+yfv16AL777js+//xzLr744sO+xu12U1hYWGcJOTXDTNZ2NijMiIiINEpI98zcddddFBQU0KNHD5xOJx6Ph0ceeYQrrrjisK+ZOnUqDz30UBCrbIBW3TGWgxSKyd+zA4/X4HRYdlclIiISlkK6Z+btt9/m9ddf580332T58uXMnDmT//u//2PmzJmHfc0999xDQUGBf8nOzg5ixccoMgZSOgPQybuVHfvLbC5IREQkfIV0z8ydd97J3XffzeWXXw5Anz592Lp1K1OnTmXChAmHfI3L5cLlcgWzzAax0k+CvE2caG1nQ24RHdJi7S5JREQkLIV0z0xpaSkOR90SnU5n2J+aDfgnAXe3snV6toiISCM0qGcmOzsby7Jo3749AF9//TVvvvkmPXv25Prrrw9YcSNHjuSRRx6hQ4cO9OrVixUrVvD0009z7bXXBuw9bFPrWjNvKsyIiIg0WIN6Zq688koWLFgAQE5ODhdccAFff/01f/jDH3j44YcDVtxzzz3H2LFjufnmmznppJO44447uOGGG/jTn/4UsPewjb9nRmc0iYiINEaDwsyaNWsYMGAAAP/617/o3bs3S5Ys4c0332TGjBkBKy4hIYFp06axdetWysrK2LRpE3/+85+JiooK2HvYJq0LxhFJvFVOSe5mvF5jd0UiIiJhqUFhprKy0j/J9pNPPuHSSy8FoEePHuzatStw1TVnzkj/TSezqrayI19nNImIiDREg8JMr169eOmll1i8eDHz5s1j2LBhAOzcuZO0tLSAFticWW2qrwRsZfPDrhC8uJ+IiEgYaFCYefzxx/nrX//K4MGDueKKK+jXrx8Ac+bM8Q8/yTGongTczbGdtQozIiIiDdKgs5kGDx7M3r17KSwsJCUlxb/++uuvJzZW10s5ZjX3aLK287HCjIiISIM0qGemrKwMt9vtDzJbt25l2rRprFu3jvT09IAW2Ky17gFAN2sH63fm21uLiIhImGpQmBk1ahSvvfYaAPn5+QwcOJCnnnqK0aNH8+KLLwa0wGYtpTMmMhaXVYkzfxNF5ZV2VyQiIhJ2GhRmli9fzjnnnAPAv//9b9q0acPWrVt57bXXePbZZwNaYLPmcGBl9AGgl7WFdTm63oyIiMjxalCYKS0tJSEhAYCPP/6YMWPG4HA4OOOMM9i6dWtAC2z2MvoC0MuxVZOARUREGqBBYaZr167Mnj2b7Oxs5s6dy4UXXghAbm4uiYmJAS2w2WvrCzM9rS38sEs9MyIiIserQWHmj3/8I3fccQedOnViwIABDBo0CPD10pxyyikBLbDZqxlmcmxl7c4Cm4sREREJPw06NXvs2LGcffbZ7Nq1y3+NGYChQ4dy2WWXBay4FiG9J8YRQYq3mMLdW/B4z8LpsOyuSkREJGw0KMwAZGRkkJGRwfbt27Esi3bt2umCeQ0R4YLWJ8Lu7znB8xNb95VwQut4u6sSEREJGw0aZvJ6vTz88MMkJSXRsWNHOnToQHJyMn/605/wer2BrrHZs2omAVtbWKt5MyIiIselQWHm3nvvZfr06Tz22GOsWLGC5cuX8+ijj/Lcc89x//33B7rG5s9/RtMWndEkIiJynBo0zDRz5kz+8Y9/+O+WDdCvXz/atWvHzTffzCOPPBKwAluEmjOaHFt5W2FGRETkuDSoZyYvL48ePXrUW9+jRw/y8vIaXVSL06Y3AO2tvWzfucPmYkRERMJLg8JMv379mD59er3106dPp2/fvo0uqsWJScab1BGA1OL17C1221yQiIhI+GjQMNMTTzzBJZdcwieffMKgQYOwLIslS5aQnZ3N//73v0DX2CI4MvtCwVZ6WltYvaOAISfqhp0iIiLHokE9M+eeey7r16/nsssuIz8/n7y8PMaMGcP333/Pq6++GugaW4YM3/V6ejm2snq7Lp4nIiJyrBp8nZnMzMx6E32/++47Zs6cySuvvNLowlqcWjec/J/CjIiIyDFrUM+MNIG2vp6ZrtYONmzPsbkYERGR8KEwEyoS2+JNaIvTMqQXr2N3YbndFYmIiIQFhZkQ4mh3GgB9HZs0b0ZEROQYHdecmTFjxhxxe35+fmNqkcxT4Mf/crJjE6t2FHB+zzZ2VyQiIhLyjivMJCUlHXX71Vdf3aiCWrSanhnrJ97bnm9vLSIiImHiuMKMTrtuYpmnANDRkcu27dkYczqWZdlclIiISGjTnJlQEpOMN7UrAO3L1rGrQJOARUREjkZhJsQ42p0KQD9rE6s0CVhEROSoFGZCTe0zmnbk21uLiIhIGFCYCTU1PTOOTazKzre3FhERkTCgMBNqMvpgrAhaW4XkbN+E12vsrkhERCSkKcyEmsgYaNMTgK4V69i4p9jmgkREREKbwkwIsvxDTT/x7db9NlcjIiIS2hRmQlH1JOB+1iaFGRERkaNQmAlF7U8HfJOAv9u6x+ZiREREQpvCTChqdSJeVxKxlpvofWvJK6mwuyIREZGQpTATihwOHB3OAOB0xzpWbNNQk4iIyOEozISqDgMBOM2xTvNmREREjkBhJlR1GATA6Y71fLslz+ZiREREQpfCTKjKPAXjiCTdyidvxwYqPV67KxIREQlJIR9mduzYwa9+9SvS0tKIjY3l5JNP5ttvv7W7rKYXGQOZpwDQx/MDP+4qsrkgERGR0BTSYWb//v2cddZZREZG8uGHH/LDDz/w1FNPkZycbHdpQWFVz5vp71jPt1s11CQiInIoEXYXcCSPP/44WVlZvPrqq/51nTp1sq+gYOswCJY8R3/HOp7dup+JZ3W2uyIREZGQE9I9M3PmzKF///784he/ID09nVNOOYW///3vR3yN2+2msLCwzhK2snw9M90dO1i3eSvG6KaTIiIiBwvpMPPTTz/x4osv0q1bN+bOncuNN97IrbfeymuvvXbY10ydOpWkpCT/kpWVFcSKAyyuFd60bgBklaxhy75SmwsSEREJPZYJ4f/dj4qKon///ixZssS/7tZbb2XZsmV8+eWXh3yN2+3G7Xb7nxcWFpKVlUVBQQGJiYlNXnPA/WcSrPgnL1RdSuqlj3D5gA52VyQiItLkCgsLSUpKOqbf75DumWnbti09e/ass+6kk05i27Zth32Ny+UiMTGxzhLWqq83M8DxI19t1iRgERGRg4V0mDnrrLNYt25dnXXr16+nY8eONlVkg05nAb47aH+3aYfmzYiIiBwkpMPMb3/7W5YuXcqjjz7Kxo0befPNN/nb3/7GLbfcYndpwZPSCW9SFpGWh3bFq9i+v8zuikREREJKSIeZ008/nffff59Zs2bRu3dv/vSnPzFt2jTGjx9vd2lB5eh0DgCDHD+w9Kd9NlcjIiISWkL6OjMAI0aMYMSIEXaXYa/O58B3b3KG4wfe+CmPX/QP4zO0REREAiyke2akWqezAehr/cSqn7bbXIyIiEhoUZgJB8kd8CZ3JMLy0q5wJTvyNW9GRESkhsJMmKiZN3OGYy1fad6MiIiIn8JMuOhcE2a+58tNCjMiIiI1FGbCRfW8mT7WZlZs2KbrzYiIiFRTmAkXSe3xpnTGaRmyir9j054SuysSEREJCQozYcRRPdR0puN7Pt+wx+ZqREREQoPCTDjpfC4AZztW8/nGvTYXIyIiEhoUZsJJl/MwWJzkyGbTpg1Uerx2VyQiImI7hZlwEpsKmacC0N+zghXb8u2tR0REJAQozIQZq9v5AJzrWKV5MyIiIijMhJ+uvjBztmM1n6/fbXMxIiIi9lOYCTeZp+J1JZFslWDt/JaC0kq7KxIREbGVwky4cUbg6DIEgHMcq/jyJ53VJCIiLZvCTDjqemDezMJ1mjcjIiItm8JMOOo6FIB+1ia+XbsJr1e3NhARkZZLYSYcJWbibX0SDsvQo/Qbvt9ZaHdFIiIitlGYCVOObhcAMNi5kk9/1FlNIiLScinMhKsThwNwnmMlC9fusrkYERER+yjMhKv2A/BGp5JiFRO1cxm5heV2VyQiImILhZlw5YzAceJFAJzvXM78H3NtLkhERMQeCjPhrHqo6QLHN3y6VvNmRESkZVKYCWddzsPriKKzYzc7Nq6ivNJjd0UiIiJBpzATzlwJWJ3PAeAc7zK+/GmfzQWJiIgEn8JMmLOqh5rOd37Lx9/n2FyNiIhI8CnMhLvqMHOatYGv16zHo6sBi4hIC6MwE+6S2mMy+uKwDKe6v+LrzXl2VyQiIhJUCjPNgHXSSAAudnzFR2t0AT0REWlZFGaag16XAXC2Yw1L1mzQjSdFRKRFUZhpDlp1w9umN5GWh5NLl7AiO9/uikRERIJGYaaZcFT3zoxwLNVQk4iItCgKM81FdZg5y7GGL1avxxgNNYmISMugMNNcpHXB26YvEZaXPkWfs2ZHod0ViYiIBIXCTDPi6O3rnbnEsZQ53+2wuRoREZHgUJhpTnqNBuBMx/csWrFWF9ATEZEWQWGmOUk9AW/bk4mwvAws+4wvN+leTSIi0vwpzDQzjr7jABjj/Jz3V2ioSUREmj+Fmeamz1iM5eQUx0Z+XPMtZRUeuysSERFpUgozzU18OnQ9H4Dh3oXMW7vb5oJERESalsJMM2T1uxyAy5yf85/l2TZXIyIi0rTCKsxMnToVy7KYMmWK3aWEthMvxhOVSDtrH+6Nn7Gv2G13RSIiIk0mbMLMsmXL+Nvf/kbfvn3tLiX0RUbj7DMGgFHWZ5oILCIizVpYhJni4mLGjx/P3//+d1JSUuwuJzz0uwKA4c6vmf31Bt3eQEREmq2wCDO33HILl1xyCeeff/5R93W73RQWFtZZWqSsgXiTOxNvlXNS3ics37bf7opERESaRMiHmbfeeovly5czderUY9p/6tSpJCUl+ZesrKwmrjBEWRaO/hMBuMo5j7e+1kRgERFpnkI6zGRnZ3Pbbbfx+uuvEx0dfUyvueeeeygoKPAv2dkt+Ef8lF/hdUTR17GZLasWU1ReaXdFIiIiARfSYebbb78lNzeX0047jYiICCIiIli0aBHPPvssEREReDz1LwjncrlITEyss7RYca2weo8G4BfmY/7fd7vsrUdERKQJhHSYGTp0KKtXr2blypX+pX///owfP56VK1fidDrtLjHkWf1/A8BI55f896vvba5GREQk8CLsLuBIEhIS6N27d511cXFxpKWl1Vsvh5E1gKrWPYnZ8wM9dn/A6u1n0qd9kt1ViYiIBExI98xIAFgWEQN8vTPjnZ/w2pKfbC5IREQksEK6Z+ZQFi5caHcJ4afvL/F8/Ee6VO4ib/VH7Lu4J2nxLrurEhERCQj1zLQErgQcp10NwET+y1vLWvAZXiIi0uwozLQQ1hk34bWcnONcw9IlC6nyeO0uSUREJCAUZlqK5A6Yky4FYHT5bD7+YbfNBYmIiASGwkwL4jxzMgAjHUv4z2ff2lyNiIhIYCjMtCTtT6MicyBRloe+u95mZXa+3RWJiIg0msJMCxN1zq0AjHd+yj8+XW1zNSIiIo2nMNPSnDiciqROJFslpG54hw27i+yuSEREpFEUZloah5OosyYB8Gvnh/x14QabCxIREWkchZmW6OTxVLmS6ejIpWzVf9iRX2Z3RSIiIg2mMNMSRcUSMfA6AG50zubvizbZXJCIiEjDKcy0VANvwuOMoY9jCzu+mcPuwnK7KxIREWkQhZmWKi4Nx4BfA3CT9S7Pfbre5oJEREQaRmGmBbPOnIzHGc2pjo3s/WY22XmldpckIiJy3BRmWrKEDJxn3gLAHc5ZPDtvrc0FiYiIHD+FmZburNuocqXQ1bGTyFVvsDG32O6KREREjovCTEsXnUTEkLsAmBLxLtPnrrS3HhERkeOkMCPQ/1oqEjqQbuXT/scZfL05z+6KREREjpnCjECEi6gLHwDgpog5PP+fRXi9xuaiREREjo3CjPj0/jmV7QYQZ7n5xb6X+Pe32+2uSERE5JgozIiPZRE58mm8OBjhXMqCD9+hqLzS7qpERESOSmFGDsjog+nvu5De7VX/4IVPdKq2iIiEPoUZqcM59F4qXKl0c+zALH2RH3MK7S5JRETkiBRmpK6YFKKG/QmA25z/5i//mqvJwCIiEtIUZqS+k8fjzjqHGKuCiXv/jzeWbra7IhERkcNSmJH6LAvXmOlUOqIZ6PiRrXOn667aIiISshRm5NBSOuG88CEAfsvrPP2veRij4SYREQk9CjNyWI4B11OacTpxlpsx2/7M219vsbskERGRehRm5PAcDmJ/8VcqnLEMdPzI7g8eZcveErurEhERqUNhRo4srQsRI58G4Bbr37z0xptUebw2FyUiInKAwowclePkKyjtMZYIy8ukvMf469zldpckIiLipzAjxyR29DMUx2XR3trLiV/eyWfrdttdkoiICKAwI8cqOpH48f+k0orifOdy1r11DzkFOl1bRETspzAjxy7zFMzIZwG4zrzLG6/+hUrNnxEREZspzMhxiTr1CgpPvgGAm/Y/xd//NdvegkREpMVTmJHjljjyUfa2OYtYy83YH3/Lu/O/sLskERFpwRRm5Pg5I2g18U32xXUl3crnlEW/ZsmqdXZXJSIiLZTCjDRMTDKp188hL7INJ1i7iHt3POuyc+yuSkREWiCFGWkwK6kd8b+eTbEVTz9rA7mvXMm2PYV2lyUiIi2Mwow0SlRGT7jyX7iJ4hzzLateuobcgjK7yxIRkRYkpMPM1KlTOf3000lISCA9PZ3Ro0ezbp3mZoSa+G5nUXbp3/DgYITnE+a9cBv7SyrsLktERFqIkA4zixYt4pZbbmHp0qXMmzePqqoqLrzwQkpKdLPDUJN86mXkD3kMgPHut/no2VvYV6SL6omISNOzjDHG7iKO1Z49e0hPT2fRokX87Gc/O6bXFBYWkpSUREFBAYmJiU1coez96DFaLZ0KwIeRF9B/0kxaJ8XZXJWIiISb4/n9DumemYMVFBQAkJqaanMlcjitht1N7uAn8OBgeOU81j93Gdv37LO7LBERacbCpmfGGMOoUaPYv38/ixcvPux+brcbt9vtf15YWEhWVpZ6ZoIs96t/k/ThjbioZJV1Is7xb9Gr6wl2lyUiImGiWfbMTJo0iVWrVjFr1qwj7jd16lSSkpL8S1ZWVpAqlNrSB46l6BfvUGTF09esI+6fF/PF18vsLktERJqhsOiZmTx5MrNnz+azzz6jc+fOR9xXPTOhpWT795S+ehmtPbvJM/EsO+1JLrr0SrvLEhGRENdsemaMMUyaNIn33nuP+fPnHzXIALhcLhITE+ssYp+49r1InryQ7TE9SLWKueDbm5n7/G8pdevUbRERCYyQDjO33HILr7/+Om+++SYJCQnk5OSQk5NDWZkuyhZOIpMzaXf7Qr5vOwaHZbhozyv8+MRQNq37zu7SRESkGQjpYSbLsg65/tVXX2XixInHdAydmh1aNn78N9ovuZdoKnCbSH7sdh19xz2AFRltd2kiIhJCjuf3O6TDTCAozISe/dlr2fHGzfQuXw5ATmR7Ii+dRlqfC2yuTEREQkWzmTMjzVNK1kn0vPNTPu01lT0miYzK7aS9O5ZNf70ST1Gu3eWJiEiYUZgRWzicDob+4mb2X/sF/4sZgddYdNn1AWVPn8KuT58HT5XdJYqISJhQmBFbde+YxbA7X+ejQa/zg+lMvCmm7eI/sOeJUyla8R4071FQEREJAIUZsZ3DYXHxsBGkTvmcf7WaRJ6Jp7V7Kwn/uYbdz5xN+YaFdpcoIiIhTGFGQkZGSjy/nPQIGy//nFnR4yg1LtoUriH6jVFsfXY4pZu/Uk+NiIjUo7OZJCR5vIYPlqzEs/BxRlR+TKTlAWBPbDfizphA7GlXQlyazVWKiEhT0anZtSjMhLcqj5dPv/gSPnuSwZWf47IqfeutSEo7X0TioGugyxBwOG2uVEREAklhphaFmebB4zV8/O2PbF4wk7OLP6KvY7N/W7GrDd4+40jsPw7a9ILDXGxRRETCh8JMLQozzYsxhmVb9jP300/I2vouoxyfk2IV+7fnx3Ykovdo4k/5OWT0VbAREQlTCjO1KMw0X/tLKvh41VZ2L3uPE/d8zGDHd/5hKICyyBSqsgYR3/1crE5nQXovcGjOu4hIOFCYqUVhpmXYU+Rm3sqN7PnmP3TPm89gx3fEWHXvzF0ZmYBpfzpRnQZB1gBodxq4EmyqWEREjkRhphaFmZZnV0EZ81Zls3XN58TtXMpprOU0x3rirfI6+xkcVLY6ichu52F1GQxpXSGpg3pvRERCgMJMLQozLVtpRRVLNu5j0Y872bNpBa3zv6O/Yz2nOdbT3tpbb39PVCJWu1NwZJ4CGX18S2oXcEbYUL2ISMulMFOLwozUtr+kgm+37mfZ1jw2bdpIfM5X/MxaQW9rMx2t3Dpzbmp4nVGY1ifhzOgNrXtAQltIaAOJ7SC5o4KOiEgTUJipRWFGjqS80sOq7QUs25LHis255G9bTdfK9fS1fqKHYxsnWtnEWe7Dvt44IiG1C1brbtDqRGjVHZI7QHw6xLX2zcnRGVUiIsdNYaYWhRk5Hl6vYWteKWt2FPD9zkJ+2LGfgl2byCjbSA9rG50du0gnn3Qrn3bW3nqTjOsdLzoFK70HVusekJwF8W0gLh0iYyAmxRd+IqKC9OlERMKHwkwtCjMSCPtLKtiQW8z63UVszC1mW14p2fuKqdyfTQfvdrpaO+li7aSrYwcZ5NHKKjhij04NYzkx0UlYrngsV6Iv4MSk+Hp10rpAWjdI6QTRSb5ensgY9fSISItwPL/fGuwXOQYpcVEM6JzKgM6pddZ7vYY9xW625ZWybV8pS/JKyc4rZVteKXvz9hFbvI1u1na6OnaSQR6trQLSrAJcVJJh7SeRUqyyPCjLO6Y6jOWAyDhwJWBFxYErHqLiISrO9zc6qf4SkwyuJIhOrA5FiRAZ3QStJCJiD4UZkUZwOCzaJEbTJjGa0zul1tteXulh+/4ydheWs6ugnDUFZeQUlpNTUM6u/DKqCnbhKcsngTISrFKSKSHZKiLdyqeztYsu1i4yrb3EU47DMljGCxVFvqURjNMF0Ym+3qDo6qDjSvT1/ES4ICLGF3hcCRCdfCAYOaPAWwWeSt/22DSIbeX7q4AkIjZRmBFpQtGRTrqmx9M1Pf6w+7irPOwrriCvpIJ9JRXsK3aTV1LBmpIKPiv2rcsrLqespJDy0kKMu5g4yn2LVU48ZcRabuIpJdEqI4FSkqwSEikl0SohkRISrDISKSWeMl8o8rihZI9vCRATEYuJjMHCYHkqwVMBEdEQFevrOYqs/lvzODLGF44iXOB0gTOy+nH1uojoWkNrDt8S4fKFLleib3/LAqz6f2uOFVEdzjQ0J9KsKcyI2MwV4SQzOYbM5Jhj2r+iysv+0grySyspKq+kqLyKwvJKCsurKCqvZE95FT+VV1JYVuXfXlS9rbi8AstdTLzxBZyE6sBT0zPkopJoKoi2KoimgnjKSLRKq/crJYpKqnBSRQTRuEmzikihiEjLg1VVilVVWrdYjxvcBU3QasfD8gWjyGjfX2ckOCLrhquoOF9ockT67sDuiKi1HOZ5bCrEpPqeW07fxRYtpy901azzP661rs5fx4Fj1llX/R519tfFHEUOR2FGJMxERTj8Q1sNYYyhvNJLSUUVJe4qit1VlFZ4KHb7npe6PdXrqtjr9lBa4dteVlHrcaXHv67EXUlkVRHRVYXEUIEXiwoiqTJOXFYlsbiJpZxYq/ovbmKtclxUEkUlLquKKCqJpIoofI+jrCpcVJBAGTGWGwtw4MVFJQmUkmCVEYHH1wuE8W/39b8YIvHgsGrObTBQVeZbwl6tHiZXgm/oLyoOyvJ9PWGRMb7AZjl8w4HG4+udqhlGdDjBGMDU+us98NhT5Zu/ZTl8x3cl+OZiRcYe6N2yHHXDn7fS18PnqaruDavpXYs+6HGUrwfOv0/1X0cEVJVDZZmv3qh4X60RUb5jeip8i/H454vhjIKCbN9wZ1Rs3WDqSjz0JRGqKqCiGNyFvvdyRPqCbWSML5TqelFhTf96Ii2MZVnERDmJiXLSKt4VsOMaY3BXeSmv9FBeWf23yhd4yiu9VHi8VFRVLx6P/7G7yretuMpbd13Vgde4/Y89ddZX1NrPXeu1NYEmGl8Pk6u6pymKKn9oirYqiK0erou13MTgxokXJ14i8OC0PETgxYmHSDw4qfXcqiKFYpKtYhwYHNWvq/3YwviP57C8Bx5z4HHtdZGW51ha+cBDd6Fvqa055LVAsBwHQk1VObiLfH+PJDrZF6S8lb6Q5K0Cr8cXBJ2RvjDkD1Ze376WwxeyvB7fX6ju3Yuo7gGs9df/uGZ9RP3HB+9rDBTl+GqJjD4wl632X/B9Dyyr+rWRB713dW+j8UJxrm+9q/qkAa8Hqty+zxyT4guFxutbqDme40APob+30FG359AR4buYaHJWU/6rHpHCjIgEhGVZREc6iY502lqHMYZKjzlE4PEcCEkHB6JaYajS46XKY6j0+v6We7xUeg1VHi+VHkNV9fpKj8HjPbDN95rqdZ7qdd4D+9U+5qHWVXkNFkcOPTUsDHFWOYmUEGeVU2DiqCDSH94APDjw4iDaqvDNm6IMh+X19WUZCwPV8Yvqvi2LKhzk45vflUAZ8ZQRb5X5jwkQgac6CLpx4sGLgzyTQAURRFGFy/L1uEXh612r6XlzVa/z9cYd2CcCD26iKCMKDw7i8M0Dc1mVVJoIKoigqrpFYnD7h0N3mTTKiCIWNzHVYTSeMqIsj+/HuDzftxykDBfluIjwB9tKXyscZn8qSw79RassPfT6Fqr8jNuIHvawbe+vMCMizYplWURFWERFOCBwHU9NzhhfoPF4Tb1A5V9Xa1uVt3ZgqrXOH5i81c99+3m8BmPAY3zH83oNHlPrr/FdaiDzoPUVXnBXv+bAet++Hq/Ba3yL56D1Nft6Tf39a7Ybg+9xrfUH/uKv4eD1R2hFXFSSSEn1XK8yyomiyMRQRAwlxOChbth24CWZYlKsImJxU4WTSpz+AJVICRF4qSCieonEAHG4ceDFg8MfHAGceImsDmn+xTrQuxfpX19FBN7qbb79a2/39dQZ9phk3LWC6oE5bZXE4LuWVSGxANWvrTrwHtaBGgywzyThwEus5SaOcqpw4CYKLw6SKcJFpX/g1vL3MhqclqdOyI6oFbYd1T2Zm3Yazgv4fxXHTmFGRCQEWJZFpNMi0ontvVuhzJiDQpk/nFEdykzdEOQ9sK8xh15f+zX+49QLZYdeXzsIHhzW/K87KIyZ2oHN+OqqMoYKA6UH1WDwvb7M/9moDoJ1j+H1f0bf6JR/nQGqazT4Xu+tbkND7Rp8ddXUW9PWHv++Ne1PnXasWW7I6qIwIyIiciwsy/JND8EX/EQAdK6fiIiIhDWFGREREQlrCjMiIiIS1hRmREREJKwpzIiIiEhYU5gRERGRsKYwIyIiImFNYUZERETCmsKMiIiIhDWFGREREQlrCjMiIiIS1hRmREREJKwpzIiIiEhYU5gRERGRsBZhdwFNzRgDQGFhoc2ViIiIyLGq+d2u+R0/kmYfZoqKigDIysqyuRIRERE5XkVFRSQlJR1xH8scS+QJY16vl507d5KQkIBlWQE9dmFhIVlZWWRnZ5OYmBjQYzcHap+jUxsdmdrn6NRGR6b2ObpQbSNjDEVFRWRmZuJwHHlWTLPvmXE4HLRv375J3yMxMTGkvgChRu1zdGqjI1P7HJ3a6MjUPkcXim10tB6ZGpoALCIiImFNYUZERETCmsJMI7hcLh544AFcLpfdpYQktc/RqY2OTO1zdGqjI1P7HF1zaKNmPwFYREREmjf1zIiIiEhYU5gRERGRsKYwIyIiImFNYUZERETCmsJMA73wwgt07tyZ6OhoTjvtNBYvXmx3SbZ48MEHsSyrzpKRkeHfbozhwQcfJDMzk5iYGAYPHsz3339vY8VN77PPPmPkyJFkZmZiWRazZ8+us/1Y2sTtdjN58mRatWpFXFwcl156Kdu3bw/ip2g6R2ufiRMn1vtOnXHGGXX2ac7tM3XqVE4//XQSEhJIT09n9OjRrFu3rs4+Lf07dCxt1JK/Ry+++CJ9+/b1XwRv0KBBfPjhh/7tzfH7ozDTAG+//TZTpkzh3nvvZcWKFZxzzjkMHz6cbdu22V2aLXr16sWuXbv8y+rVq/3bnnjiCZ5++mmmT5/OsmXLyMjI4IILLvDfM6s5KikpoV+/fkyfPv2Q24+lTaZMmcL777/PW2+9xeeff05xcTEjRozA4/EE62M0maO1D8CwYcPqfKf+97//1dnenNtn0aJF3HLLLSxdupR58+ZRVVXFhRdeSElJiX+flv4dOpY2gpb7PWrfvj2PPfYY33zzDd988w3nnXceo0aN8geWZvn9MXLcBgwYYG688cY663r06GHuvvtumyqyzwMPPGD69et3yG1er9dkZGSYxx57zL+uvLzcJCUlmZdeeilIFdoLMO+//77/+bG0SX5+vomMjDRvvfWWf58dO3YYh8NhPvroo6DVHgwHt48xxkyYMMGMGjXqsK9pSe1jjDG5ubkGMIsWLTLG6Dt0KAe3kTH6Hh0sJSXF/OMf/2i23x/1zByniooKvv32Wy688MI66y+88EKWLFliU1X22rBhA5mZmXTu3JnLL7+cn376CYDNmzeTk5NTp61cLhfnnntui22rY2mTb7/9lsrKyjr7ZGZm0rt37xbTbgsXLiQ9PZ3u3btz3XXXkZub69/W0tqnoKAAgNTUVEDfoUM5uI1q6HsEHo+Ht956i5KSEgYNGtRsvz8KM8dp7969eDwe2rRpU2d9mzZtyMnJsakq+wwcOJDXXnuNuXPn8ve//52cnBzOPPNM9u3b528PtdUBx9ImOTk5REVFkZKScth9mrPhw4fzxhtvMH/+fJ566imWLVvGeeedh9vtBlpW+xhjuP322zn77LPp3bs3oO/QwQ7VRqDv0erVq4mPj8flcnHjjTfy/vvv07Nnz2b7/Wn2d81uKpZl1XlujKm3riUYPny4/3GfPn0YNGgQXbp0YebMmf7Jdmqr+hrSJi2l3caNG+d/3Lt3b/r370/Hjh354IMPGDNmzGFf1xzbZ9KkSaxatYrPP/+83jZ9h3wO10Yt/Xt04oknsnLlSvLz83n33XeZMGECixYt8m9vbt8f9cwcp1atWuF0Ouul09zc3HpJtyWKi4ujT58+bNiwwX9Wk9rqgGNpk4yMDCoqKti/f/9h92lJ2rZtS8eOHdmwYQPQctpn8uTJzJkzhwULFtC+fXv/en2HDjhcGx1KS/seRUVF0bVrV/r378/UqVPp168ff/nLX5rt90dh5jhFRUVx2mmnMW/evDrr582bx5lnnmlTVaHD7Xazdu1a2rZtS+fOncnIyKjTVhUVFSxatKjFttWxtMlpp51GZGRknX127drFmjVrWmS77du3j+zsbNq2bQs0//YxxjBp0iTee+895s+fT+fOnets13fo6G10KC3te3QwYwxut7v5fn9smHQc9t566y0TGRlpXn75ZfPDDz+YKVOmmLi4OLNlyxa7Swu63/3ud2bhwoXmp59+MkuXLjUjRowwCQkJ/rZ47LHHTFJSknnvvffM6tWrzRVXXGHatm1rCgsLba686RQVFZkVK1aYFStWGMA8/fTTZsWKFWbr1q3GmGNrkxtvvNG0b9/efPLJJ2b58uXmvPPOM/369TNVVVV2fayAOVL7FBUVmd/97ndmyZIlZvPmzWbBggVm0KBBpl27di2mfW666SaTlJRkFi5caHbt2uVfSktL/fu09O/Q0dqopX+P7rnnHvPZZ5+ZzZs3m1WrVpk//OEPxuFwmI8//tgY0zy/PwozDfT888+bjh07mqioKHPqqafWOSWwJRk3bpxp27atiYyMNJmZmWbMmDHm+++/92/3er3mgQceMBkZGcblcpmf/exnZvXq1TZW3PQWLFhggHrLhAkTjDHH1iZlZWVm0qRJJjU11cTExJgRI0aYbdu22fBpAu9I7VNaWmouvPBC07p1axMZGWk6dOhgJkyYUO+zN+f2OVTbAObVV1/179PSv0NHa6OW/j269tpr/b9PrVu3NkOHDvUHGWOa5/fHMsaY4PUDiYiIiASW5syIiIhIWFOYERERkbCmMCMiIiJhTWFGREREwprCjIiIiIQ1hRkREREJawozIiIiEtYUZkSkxbEsi9mzZ9tdhogEiMKMiATVxIkTsSyr3jJs2DC7SxORMBVhdwEi0vIMGzaMV199tc46l8tlUzUiEu7UMyMiQedyucjIyKizpKSkAL4hoBdffJHhw4cTExND586deeedd+q8fvXq1Zx33nnExMSQlpbG9ddfT3FxcZ19XnnlFXr16oXL5aJt27ZMmjSpzva9e/dy2WWXERsbS7du3ZgzZ07TfmgRaTIKMyIScu6//35+/vOf89133/GrX/2KK664grVr1wJQWlrKsGHDSElJYdmyZbzzzjt88skndcLKiy++yC233ML111/P6tWrmTNnDl27dq3zHg899BC//OUvWbVqFRdffDHjx48nLy8vqJ9TRALE7jtdikjLMmHCBON0Ok1cXFyd5eGHHzbG+O6IfOONN9Z5zcCBA81NN91kjDHmb3/7m0lJSTHFxcX+7R988IFxOBwmJyfHGGNMZmamuffeew9bA2Duu+8+//Pi4mJjWZb58MMPA/Y5RSR4NGdGRIJuyJAhvPjii3XWpaam+h8PGjSozrZBgwaxcuVKANauXUu/fv2Ii4vzbz/rrLPwer2sW7cOy7LYuXMnQ4cOPWINffv29T+Oi4sjISGB3Nzchn4kEbGRwoyIBF1cXFy9YZ+jsSwLAGOM//Gh9omJiTmm40VGRtZ7rdfrPa6aRCQ0aM6MiIScpUuX1nveo0cPAHr27MnKlSspKSnxb//iiy9wOBx0796dhIQEOnXqxKeffhrUmkXEPuqZEZGgc7vd5OTk1FkXERFBq1atAHjnnXfo378/Z599Nm+88QZff/01L7/8MgDjx4/ngQceYMKECTz44IPs2bOHyZMnc9VVV9GmTRsAHnzwQW688UbS09MZPnw4RUVFfPHFF0yePDm4H1REgkJhRkSC7qOPPqJt27Z11p144on8+OOPgO9Mo7feeoubb76ZjIwM3njjDXr27AlAbGwsc+fO5bbbbuP0008nNjaWn//85zz99NP+Y02YMIHy8nKeeeYZ7rjjDlq1asXYsWOD9wFFJKgsY4yxuwgRkRqWZfH+++8zevRou0sRkTChOTMiIiIS1hRmREREJKxpzoyIhBSNfIvI8VLPjIiIiIQ1hRkREREJawozIiIiEtYUZkRERCSsKcyIiIhIWFOYERERkbCmMCMiIiJhTWFGREREwprCjIiIiIS1/w+PXNxyjM+USAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 1s 10ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.88      0.87       176\n",
      "           1       0.74      0.57      0.64       166\n",
      "           2       0.68      0.78      0.72       171\n",
      "           3       0.98      0.96      0.97       169\n",
      "           4       0.93      1.00      0.96       156\n",
      "           5       0.97      0.98      0.97       162\n",
      "\n",
      "    accuracy                           0.86      1000\n",
      "   macro avg       0.86      0.86      0.86      1000\n",
      "weighted avg       0.86      0.86      0.86      1000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'dnn_model_20250816_133711.h5'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#模型训练\n",
    "train_model(df,y,df_val,y_val)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
